Bone structure contour extraction device, image processing device, bone structure contour extraction method, image processing method, and program

Abstract

To solve such a problem that a bone part cannot be properly detected from a chest transmission image in a conventional technique.SOLUTION: A bone structure contour extraction apparatus A includes: a clavicle line acquisition part 32 which acquires a lower clavicle line and an upper clavicle line by using a chest transmission image; and a clavicle line output part 41 which outputs the lower clavicle line and the upper clavicle line. The clavicle line acquisition part 32 includes: candidate straight line detection means 321 which detects one or more candidate straight lines being candidates of a border line of the clavicles by analyzing the chest transmission image; partial straight line detection means 322 which detects as a partial straight line constituting the lower clavicle line the candidate straight line closest to a pixel of a maximum pixel value in the pixels in a range matching a clavicle inclination condition and a clavicle line region condition in the chest transmission image in the one or more candidate straight lines; clavicle extension means 323 which detects the lower clavicle line in which the partial straight line is extended; and second clavicle line detection means 324 which detects a boundary point on the opposite side satisfying a clavicle width condition with respect to each point constituting the lower clavicle line and detects the upper clavicle line including the boundary point.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a structural contour extraction device that detects bone parts from chest transmission images and the like.

Background Art

[0002] Conventionally, in reading chest X-ray films, in order to avoid a decrease in the visibility of lung lesions caused by the overlapping of structures such as ribs and collarbones, a bone tissue transmission technology applying an artificial intelligence algorithm has already been commercialized (see Non-Patent Document 1).

[0003] More specifically, the conventional technology is a technology for obtaining a chest transmission image from which collarbones and ribs are removed from a chest X-ray film by performing prediction processing using deep learning in machine learning based on a database of various foreigners centered in Europe and America.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, since there are significant differences in physique between Japanese and Westerners, it is expected that in many cases, the conventional method using machine learning cannot appropriately detect bone parts such as collarbones or ribs from chest transmission images of Japanese people.

[0006] Furthermore, when using machine learning techniques, in order to appropriately detect bony areas such as the clavicle or ribs from transcatheter images of Japanese people's chests, it is necessary to create a large amount of training data based on transcatheter images of Japanese people's chests and then perform machine learning training. [Means for solving the problem]

[0007] The bone structure contour extraction device of the first invention comprises an image storage unit for storing a chest transmission image, a clavicle line acquisition unit for acquiring an inferior clavicle line, which is the lower boundary line of the clavicle, and an superior clavicle line, which is the upper boundary line of the clavicle, using the chest transmission image, and a clavicle line output unit for outputting the inferior clavicle line and the superior clavicle line. The clavicle line acquisition unit includes a candidate line detection means for analyzing the chest transmission image and detecting one or more candidate lines that are candidates for the boundary line of the clavicle, and among the one or more candidate lines, a means for detecting lines that match the clavicle inclination condition and the clavicle line region condition in the chest transmission image. The bone structure contour extraction device comprises: a partial line detection means that detects the pixel with the highest pixel value among pixels in a range that matches the criteria and detects the candidate line closest to the pixel as a partial line constituting the lower clavicle line or the upper clavicle line; a clavicle extension means that detects the lower clavicle line or the upper clavicle line extended from the endpoint of the partial line; and a second clavicle line detection means that detects the opposite boundary point that satisfies the clavicle width condition for each point constituting the lower clavicle line or the upper clavicle line and detects the upper clavicle line or the lower clavicle line that includes the boundary point.

[0008] This configuration allows for the proper detection of the clavicle from chest radiographic images. In particular, this configuration enables the proper detection of the clavicle from chest radiographic images without requiring the acquisition of a large amount of training data based on chest radiographic images.

[0009] Furthermore, the bone structure contour extraction device of the second invention, compared to the first invention, is a bone structure contour extraction device in which the clavicle extension means calculates an upper difference, which is the difference between the pixel value of a pixel above a candidate point and the pixel value of the candidate point, and a lower difference, which is the difference between the pixel value of the candidate point and the pixel value of a pixel below the candidate point, from among one or more candidate points that extend a partial straight line, and selects a candidate point in which the larger of the upper difference and the lower difference is greater than or equal to a threshold, and the difference between the upper representative value, which is the representative value of the pixel values ​​of two or more pixels above the candidate point, and the lower representative value, which is the representative value of the pixel values ​​of two or more pixels below the candidate point is greater than or equal to a threshold, and adopts the candidate point as a point that constitutes the lower clavicle line or the upper clavicle line, and detects the lower clavicle line or the upper clavicle line.

[0010] This configuration allows for the proper detection of the clavicle from chest transmission images.

[0011] Furthermore, the bone structure contour extraction device of this third invention, compared to the second invention, is a bone structure contour extraction device in which the clavicle extension means selects one or more candidate points from among the points advanced one step in the X direction from the endpoint of a partial straight line, where the change in the Y direction is within a threshold with respect to the endpoint, and adopts from among the one or more candidate points as points that constitute the inferior clavicle line or the superior clavicle line.

[0012] This configuration allows for the proper detection of the clavicle from chest transmission images.

[0013] Furthermore, the bone structure contour extraction device of this fourth invention, compared to the second or third invention, is a bone structure contour extraction device in which the clavicle extension means selects a reference candidate point considering the slope of a partial straight line, calculates the difference in Y coordinate values ​​between one or more candidate points and the reference candidate point, obtains a score for one or more candidate points using the difference, and adopts the candidate point with the best score as a point constituting the inferior clavicle line or the superior clavicle line.

[0014] This configuration allows for the proper detection of the clavicle from chest transmission images.

[0015] Furthermore, the bone structure contour extraction device of the fifth invention comprises an image storage unit for storing a translucent chest image, a rib line acquisition unit for analyzing the translucent chest image and acquiring a lower rib line, which is the lower boundary line of the ribs, and an upper rib line, which is the upper boundary line of the ribs, and a rib line output unit for outputting the lower rib line and the upper rib line. The rib line acquisition unit is a rib line acquisition means that, for each of the one or more pixels constituting the translucent chest image, acquires a representative difference value, which is the difference between an upper representative value, which is a representative value of the pixel values ​​of two or more pixels above the pixel, and a lower representative value, which is a representative value of the pixel values ​​of two or more pixels below the pixel. The pixel with the largest representative difference value is designated as the pixel constituting the lower rib line, and the pixel with the smallest representative difference value is designated as the pixel constituting the upper rib line, thereby acquiring the lower rib line and the upper rib line.

[0016] This configuration allows for the proper detection of ribs from transcatheter chest images. In particular, this configuration enables the proper detection of ribs from transcatheter chest images without requiring the acquisition of a large amount of training data based on transcatheter chest images.

[0017] Furthermore, the bone structure contour extraction device of this sixth invention, compared to the fifth invention, is a bone structure contour extraction device in which, when determining the pixels constituting the lower rib line and the pixels constituting the upper rib line, determines them from the outer pixels of the chest transmission image.

[0018] This configuration allows for the proper detection of ribs from transcatheter images of the chest.

[0019] Furthermore, the bone structure contour extraction device of this seventh invention, compared to the fifth or sixth invention, is a bone structure contour extraction device in which the rib line acquisition means acquires upper candidate points constituting the upper rib line or lower candidate points constituting the lower rib line using angle information of rib extension corresponding to the location of the ribs, acquires upper representative values ​​or lower representative values ​​only for pixels corresponding to the candidate points that are upper candidate points or lower candidate points, and adopts pixels whose upper representative values ​​or lower representative values ​​satisfy the adoption conditions as pixels constituting the rib line that is the upper rib line or lower rib line.

[0020] With such a configuration, ribs can be appropriately detected from a chest fluoroscopic image.

[0021] Further, in the bone structure contour extraction device of the eighth invention, for any one of the fifth to seventh inventions, the rib line acquisition unit is composed of pixels that make up each of the lower rib line and the upper rib line acquired by the rib line acquisition means, and determines whether or not an error condition that the difference in pixel values of two pixels at the same X position is large is satisfied. When the pixel values of the two pixels satisfy the error condition, pixels are searched in the Y direction, and a correction means for acquiring the upper rib line or the lower rib line including pixels with pixel values that do not satisfy the error condition is provided. It is a bone structure contour extraction device.

[0022] With such a configuration, ribs can be appropriately detected from a chest fluoroscopic image.

[0023] Further, the bone structure contour extraction device of the ninth invention further includes a lung field detection unit that analyzes a chest fluoroscopic image and detects a lung field portion for any one of the first to eighth inventions, and the clavicle line acquisition unit acquires a lower clavicle line and an upper clavicle line from the lung field portion, or the rib line acquisition unit acquires a lower rib line and an upper rib line from the lung field portion. It is a bone structure contour extraction device.

[0024] With such a configuration, ribs can be appropriately detected from a chest fluoroscopic image.

[0025] Further, the image processing device of the tenth invention includes an image storage unit that stores a chest fluoroscopic image, a rib line storage unit that stores information for specifying pixels that make up a lower rib line and information for specifying pixels that make up an upper rib line, an inner representative value that is a representative value of pixel values of pixels inside the rib for the lower rib line or the upper rib line, and an outer representative value that is a representative value of pixel values of pixels outside the lower rib line or the upper rib line. A correction amount related to the difference is obtained, and for rib pixels that are pixels between the lower rib line and the upper rib line, the pixel value of the rib pixel is corrected using the correction amount to obtain a corrected pixel value, the corrected pixel value is used as the pixel value of the rib pixel, and a bone transmission image obtained by transmitting ribs through the chest fluoroscopic image is obtained. It is an image processing device including a bone transmission image output unit that outputs the bone transmission image acquired by the bone transmission unit.

[0026] With such a configuration, a bone transmission image excluding the bone part can be obtained from the chest transmission image.

[0027] Further, the image processing apparatus of the eleventh invention is an image processing apparatus that, with respect to the tenth invention, the bone transmission part includes pixel value correction means for correcting the corrected pixel value using two or more other corrected pixel values when the corrected pixel value satisfies an abnormal condition compared to the two or more other corrected pixel values.

[0028] With such a configuration, an appropriate bone transmission image excluding the bone part can be obtained from the chest transmission image.

[0029] Further, the image processing apparatus of the twelfth invention is an image processing apparatus that, with respect to the tenth or eleventh invention, further includes a lesion detection unit for detecting a lesion using the bone transmission image and a lesion output unit for outputting the location of the lesion corresponding to the bone transmission image.

[0030] With such a configuration, when reading a chest transmission image, it is possible to prevent a decrease in the visibility of lung lesions caused by the overlapping of structures such as ribs and collarbones.

Advantages of the Invention

[0031] According to the bone structure contour extraction apparatus of the present invention, the bone part can be appropriately detected from the chest transmission image.

Brief Description of the Drawings

[0032] [Figure 1] Block diagram of the bone structure contour extraction apparatus A in Embodiment 1 [Figure 2] Flowchart for explaining an operation example of the bone structure contour extraction apparatus A [Figure 3] Flowchart for explaining an example of the clavicle line acquisition process [Figure 4] Flowchart for explaining an example of the clavicle extension process [Figure 5] Flowchart for explaining an example of the second clavicle line detection process [Figure 6] A flowchart illustrating an example of the rib cage line acquisition process. [Figure 7] A flowchart illustrating an example of the lower rib line extension process. [Figure 8] A flowchart illustrating an example of the process of extending the upper rib line. [Figure 9] A flowchart illustrating an example of the correction process. [Figure 10] This figure shows examples of images such as chest X-ray images. [Figure 11] A diagram to explain the same-direction dependent weighted average map. [Figure 12] Block diagram of the image processing device B in Embodiment 2 [Figure 13] A flowchart illustrating an example of the operation of the image processing device B. [Figure 14] A flowchart illustrating an example of bone translucency treatment. [Figure 15] A flowchart illustrating an example of identical bone permeability processing. [Figure 16] A flowchart illustrating an example of bone margin influence correction processing. [Figure 17] A diagram illustrating the results of the same process. [Figure 18] Overview of the computer system in the above embodiment [Figure 19] Block diagram of the computer system [Modes for carrying out the invention]

[0033] The embodiments of the bone structure contour extraction device and the like will be described below with reference to the drawings. In the embodiments, components that are denoted by the same reference numerals perform the same operation, and therefore, further explanation may be omitted.

[0034] (Embodiment 1) In this embodiment, a bone structure contour extraction device that detects the outline of the clavicle from a transcatheter image by utilizing the characteristics of the clavicle's shape will be described. The characteristics of the clavicle's shape include, for example, the inclusion of a straight line with an inclination within a specific range (e.g., 90 to 150 degrees).

[0035] Furthermore, in this embodiment, a bone structure contour extraction device that detects the contour of the ribs from a transcatheter image by utilizing the characteristics of the rib shape will be described. The characteristics of the rib shape include, for example, the property that the thickness of the ribs is relatively uniform.

[0036] Furthermore, in this embodiment, a bone structure contour extraction device that determines the lung area from a chest transmission image and detects the bone area from the lung area will be described.

[0037] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is irrelevant. Information X and information Y may be linked, may reside in the same buffer, may information X be contained in information Y, or information Y may be contained in information X, and so on.

[0038] Figure 1 is a block diagram of the bone structure contour extraction device A in this embodiment. The bone structure contour extraction device A comprises a storage unit 1, a reception unit 2, a processing unit 3, and an output unit 4. The storage unit 1 comprises an image storage unit 11. The processing unit 3 comprises a lung field detection unit 31, a clavicle line acquisition unit 32, and a rib line acquisition unit 33. The clavicle line acquisition unit 32 comprises a candidate line detection means 321, a partial line detection means 322, a clavicle extension means 323, and a second clavicle line detection means 324. The rib line acquisition unit 33 comprises a rib line acquisition means 331 and a correction means 332. The output unit 4 comprises a clavicle line output unit 41 and a rib line output unit 42.

[0039] Storage unit 1 stores various types of information. These types of information include, for example, chest transparency images (described later), angle information (described later), and various conditions (described later).

[0040] The image storage unit 11 stores chest transmission images. A chest transmission image is a transmission image of the chest of a person. A chest transmission image is usually an image of the chest taken from the front. Examples of chest transmission images include chest X-ray images, chest MRI (magnetic resonance imaging) images, and chest ultrasound images. The chest transmission image here is a digital image composed of multiple pixels. The data structure and file format of the chest transmission image are not specified. A chest transmission image is usually a grayscale image, but it may also be a color image. The size, shape, number of pixels, and color depth of the chest transmission image are not specified. A chest transmission image is usually a still image, but it may also be a moving image.

[0041] Reception Unit 2 receives various instructions and information. These instructions and information include, for example, start instructions and chest radiographic images. A start instruction is an instruction to start processing by the bone structure contour extraction device A.

[0042] Any means of inputting instructions and information is acceptable, such as a touch panel, keyboard, mouse, or menu screen.

[0043] The processing unit 3 performs various processes. These processes include, for example, those performed by the lung field detection unit 31, the clavicle line acquisition unit 32, and the costal line acquisition unit 33. Another example of these processes is accumulating the chest transmission image received by the reception unit 2 in the image storage unit 11.

[0044] The processing unit 3, for example, constructs an image in which the clavicle line and costal line are clearly indicated within the chest transparency image. The processing unit 3 constructs an image in which the clavicle line is clearly indicated using the clavicle line information acquired by the clavicle line acquisition unit 32. The processing unit 3 constructs an image in which the costal line is clearly indicated using the costal line information acquired by the costal line acquisition unit 33.

[0045] The lung area detection unit 31 analyzes the chest transmission image in the image storage unit 11 and detects the lung area. The processing performed by the lung area detection unit 31 is, for example, the processing described in Japanese Patent Application No. 2020-137637.

[0046] The clavicle line acquisition unit 32 acquires the inferior clavicle line and the superior clavicle line using a chest transmission image. Using a chest transmission image may also be done by using an image of the lung field region detected from the chest transmission image by the lung field region detection unit 31. The inferior clavicle line is the boundary line below the clavicle. The inferior clavicle line may also be information that identifies the boundary line below the clavicle. The superior clavicle line is the boundary line above the clavicle. The superior clavicle line may also be information that identifies the boundary line above the clavicle. The information that identifies the boundary line may be, for example, a set of information for two or more points (x, y). The information that identifies the boundary line may also be, for example, a set of information that defines a straight line. In other words, the data structure of the inferior clavicle line and the superior clavicle line is not specified. The points (x, y) may be, for example, relative coordinate values ​​in the chest transmission image. The points (x, y) may also be relative coordinate values ​​in the lung field region detected from the chest transmission image, etc. When the inferior and superior clavicle lines are not distinguished, or when both are included, the term "clavicular line" is used.

[0047] In this specification, the coordinate values ​​are generally defined with the upper left corner of the chest transmission image as the origin (0,0), with the Y coordinate increasing as you move downwards and the X coordinate increasing as you move to the right.

[0048] The clavicle line acquisition unit 32 acquiring the clavicle line means acquiring information that identifies the clavicle line. Acquiring information that identifies the clavicle line usually means acquiring the coordinate values ​​of two or more pixels that identify the clavicle line.

[0049] The clavicle line acquisition unit 32 preferably acquires the inferior clavicle line and the superior clavicle line from the lung field detected by the lung field detection unit 31 from the chest transmission image.

[0050] The clavicle line acquisition unit 32 preferably acquires the lower clavicle line and the upper clavicle line using candidate line detection means 321, partial line detection means 322, clavicle extension means 323, and second clavicle line detection means 324.

[0051] The candidate line detection means 321 analyzes the chest transmission image and detects one or more candidate lines. A candidate line is a line that is a candidate for the clavicle line.

[0052] The candidate line detection means 321 detects one or more candidate lines from the chest transmission image using techniques for acquiring lines from the image. Techniques for acquiring lines from the image include, for example, the Hough transform (see, for example, "URL: https: / / cvml-expertguide.net / terms / cv / image-feature-detection / hough-transform / "), multiscale Hough transform, stochastic Hough transform, and LSD (Line Segment). Examples include a Detector (see, for example, "URL:https: / / data-analysis-stats.jp / python / %e3%83%8f%e3%83%95%e5%a4%89%e6%8f%9b%e3%81%a8lsd%e3%81%ab%e3%82%88%e3%82%8b%e7%9b%b4%e7%b7%9a%e6%a4%9c%e5%87%ba%e3%81%ae%e6%af%94%e8%bc%83 / "), and the technology used by the candidate line detection means 321 is not limited. Note that the Hough transform can also be called the Hough transform.

[0053] The partial line detection means 322 detects the pixel with the highest pixel value among the pixels in the chest transmission image that match the clavicle line region condition. The clavicle line region condition is information that identifies the region in which the clavicle line may exist. The region in which the clavicle line may exist is, for example, within the upper 25% of the lung field and within the outer 60% of the lung field. The content of the clavicle line region condition is not specified. In addition, in the chest transmission image, it is generally believed that the pixel with the highest pixel value is located in the region where the clavicle and ribs intersect. Therefore, the pixel with the highest pixel value is located between the upper clavicle line and the lower clavicle line. To utilize this characteristic, the partial line detection means 322 detects the pixel with the highest pixel value.

[0054] Next, when the partial line detection means 322 detects a partial line of the lower clavicle line, it determines a candidate line from among one or more candidate lines that matches the clavicle inclination condition, is closest to the pixel with the highest pixel value, and is located below the pixel with the highest pixel value. Such a candidate line is a partial line of the lower clavicle line. The clavicle inclination condition is a condition relating to the inclination of the partial lines that constitute the clavicle line. The clavicle inclination condition is, for example, the range of inclination of the partial lines that constitute the clavicle line. In the right lung, the clavicle inclination condition is, for example, 90 to 150 degrees. Here, it is assumed that the top of the chest transmission image is 0 degrees, the right is 90 degrees, and the bottom is 180 degrees. Furthermore, this process utilizes the fact that pixels inside the clavicle have larger pixel values ​​than pixels outside it.

[0055] Furthermore, if the partial line detection means 322 detects a partial line of the upper clavicle line, it determines a candidate line from among one or more candidate lines that is closest to the pixel with the highest pixel value and is located above the pixel with the highest pixel value. Such a candidate line is a partial line of the upper clavicle line.

[0056] Furthermore, the partial straight line detection means 322 is preferable to detect either a partial straight line of the lower clavicle line or a partial straight line of the upper clavicle line. Moreover, the partial straight line detection means 322 is preferable to detect a partial straight line of the lower clavicle line.

[0057] Furthermore, if the partial line detection means 322 detects two or more pixels with the highest pixel value within the range of pixels that match the clavicle line region condition, it performs processing as follows, for example. That is, the partial line detection means 322 detects two or more pixels with the highest pixel value within a predetermined narrow range, and determines the candidate line that is closest to one of those pixels and located below the pixel with the highest pixel value as the partial line of the lower clavicle line. Alternatively, the partial line detection means 322 detects two or more pixels with the highest pixel value within a predetermined narrow range, and determines the candidate line that is closest to one of those pixels and located above the pixel with the highest pixel value as the partial line of the upper clavicle line. The predetermined narrow range is, for example, a range of X coordinates of 10 and a range of Y coordinates of 5. The detection of two or more pixels with the highest pixel value within such a predetermined narrow range utilizes the property that when multiple pixels with the highest pixel value are detected, the maximum pixel values ​​above the correct clavicle are often physically close together.

[0058] The clavicle extension means 323 detects a lower clavicle line extended from the endpoint of a partial straight line, or an upper clavicle line extended from the endpoint of a partial straight line. For example, the clavicle extension means 323 detects a lower clavicle line extended from each of the two endpoints of a partial straight line, or an upper clavicle line extended from each of the two endpoints of a partial straight line.

[0059] The clavicle extension means 323 typically detects an inferior or superior clavicle line by extending a partial straight line from its endpoint until a stopping condition is met. The stopping condition is a condition for stopping the process of extending the partial straight line. For example, the stopping condition is that the line has been extended to a point within a predetermined X-axis range in the chest radiographic image or lung field. The predetermined X-axis range point is the endpoint of the chest radiographic image or lung field, or the endpoint of the predetermined X-axis range in the chest radiographic image or lung field. For example, the stopping condition is that the length of the inferior or superior clavicle line has become greater than or equal to a threshold.

[0060] More specifically, the clavicle extension means 323 detects the inferior clavicle line, for example, as shown in (1), (2), and (3) below. It is preferable for the clavicle extension means 323 to detect the inferior clavicle line first, because the lower end is easier to detect since the upper end of the clavicle overlaps with the structure of the lung apex. (1) The clavicle extension means 323 calculates an upward difference, which is the difference between the pixel value of the pixel above the candidate point and the pixel value of the candidate point, and a downward difference, which is the difference between the pixel value of the candidate point and the pixel value of the pixel below the candidate point, from among the one or more candidate points that extend the partial straight line.

[0061] Upward difference is calculated for a candidate point (x,y) as follows: Upward difference = pixel value (x,y-1) - pixel value (x,y). Downward difference is calculated for a candidate point (x,y) as follows: Downward difference = pixel value (x,y) - pixel value (x,y+1). Note that the pixel above a candidate point is usually the pixel directly above the candidate point, but it can also be a pixel two or more pixels above. The pixel below a candidate point is usually the pixel directly below the candidate point, but it can also be a pixel two or more pixels below.

[0062] Furthermore, candidate points for extending the partial line are points (horizontal points) where the difference from the X value of the reference point is 1, and where the difference from the Y value of the point of interest is within a threshold (for example, 1 or 2). The reference point is an endpoint of the partial line, or an endpoint of a determined line. Also, when the clavicle extension means 323 determines the pixels that constitute the clavicle line, it uses those pixels as reference points and calculates the upward difference and downward difference for each of the one or more candidate points for extending the partial line.

[0063] The clavicle extension means 323 then selects the larger of the two absolute values, the upward difference and the downward difference, as the signal difference map1 value of the candidate value. The signal difference map1 value is information used to determine whether the pixel of interest (the pixel corresponding to the candidate value) is a pixel that constitutes the boundary line of the clavicle or ribs. (2) The clavicle extension means 323 calculates an upper representative value and a lower representative value for each of the one or more candidate points. The upper representative value is a representative value (e.g., sum, average) of the pixel values ​​of two or more pixels above the candidate point. The lower representative value is a representative value (e.g., sum, average) of the pixel values ​​of two or more pixels below the candidate point.

[0064] The clavicle extension means 323 calculates upper representative values ​​and lower representative values ​​for a candidate point (x,y) using, for example, the following formulas: "Upper representative value = pixel value (x,y-3) + pixel value (x,y-4) + pixel value (x,y-5)" and "Lower representative value = pixel value (x,y+3) + pixel value (x,y+4) + pixel value (x,y+5)". In calculating the upper representative value, it is sufficient to show the trend of pixel values ​​of two or more pixels above the candidate point, and several pixels and the number of pixels to select are possible. Similarly, in calculating the lower representative value, it is sufficient to show the trend of pixel values ​​of two or more pixels below the candidate point, and several pixels and the number of pixels to select are possible.

[0065] Next, the clavicle extension means 323 calculates the difference between the upper representative value and the lower representative value. Note that two or more pixels above the candidate point are two or more pixels above the candidate point. Also, two or more pixels below the candidate point are two or more pixels below the candidate point. This difference is defined as the signal difference map2 value of the candidate value. The signal difference map2 value is information regarding the difference between the trend of the pixel values ​​above the pixel of interest (the pixel corresponding to the candidate value) and the trend of the pixel values ​​below it, and can be used to determine whether or not a pixel constitutes the boundary line of the clavicle or ribs. (3) The clavicle extension means 323 selects a candidate point in which the signal difference map1 value is greater than or equal to a threshold (e.g., 7) and the signal difference map2 value is greater than or equal to a threshold (e.g., 0).

[0066] The clavicle extension means 323 then selects the candidate point as a point that constitutes the inferior clavicle line and detects the inferior clavicle line.

[0067] The clavicle extension means 323 preferably selects one or more candidate points from among the points one step in the X direction from the endpoint of the partial straight line, where the change in the Y direction is within a threshold (for example, 2) relative to the endpoint, and adopts one or more of these candidate points as points that constitute the inferior clavicle line. In other words, when extending a straight line, it is preferable to narrow down the candidate points first.

[0068] The clavicle extension means 323 is preferable to select a reference candidate point considering the slope of the partial straight line, calculate the difference in Y coordinate values ​​between each of the one or more candidate points and the reference candidate point, obtain a score for each of the one or more candidate points using this difference, and adopt the candidate point with the best score as the point that constitutes the lower clavicle line.

[0069] The clavicle extension means 323 calculates a score using an increasing function that has as a parameter the difference in the Y coordinate value between each of the one or more candidate points and a reference candidate point, and it is preferable to adopt the candidate point with the smallest score as a point that constitutes the inferior clavicle line or the superior clavicle line.

[0070] As described above, the clavicle extension means 323 is preferably determined from the inferior clavicle line. Furthermore, the clavicle extension means 323 preferably calculates the score using an increasing function that has as a parameter the difference between the maximum value (α) of the signal difference map1 value of the surrounding pixels and the signal difference map1 value of the candidate point. The clavicle extension means 323 preferably calculates the score using an increasing function that has as a parameter the difference between the Y coordinate value of the reference candidate point and the Y coordinate value of the candidate point. The Y coordinate value of the reference candidate point is the Y coordinate value using the slope of the line.

[0071] The clavicle extension means 323 uses, for example, the calculation formula "score = {α-signal difference value map1}" 2 +{Y coordinate value of reference candidate point - Y coordinate value of candidate point} 2 It is preferable to calculate a score using this method and adopt the candidate point with the lowest score as the point that constitutes the inferior clavicle line.

[0072] The variable α in the above calculation formula is the maximum value among the signal difference map1 values ​​of pixels corresponding to the number of pixels at the upper threshold and the signal difference map1 values ​​of pixels corresponding to the number of pixels at the lower threshold. Furthermore, the range of pixels from which α is obtained may be different for the inner and outer sides of the clavicle line. If the coordinates of the left end (outer end) of the determined clavicle line are (X1, Y1) and the coordinates of the right end (inner end) are (X2, Y2), then the clavicle extension means 323, for example, in the outer search for calculating α, sets α to the maximum signal difference map1 value among the signal difference map1 values ​​of the five pixels: (Y1-3, X1-1), (Y1-2, X1-1), (Y1-1, X1-1), (Y1, X1-1), and (Y1+1, X1-1). Furthermore, the clavicle extension means 323, for example, in the internal search for calculating α, sets α to the largest signal difference map1 value among the signal difference map1 values ​​of the five pixels (Y2-1,X2+1), (Y2,X2+1), (Y2+1,X2+1), (Y2+2,X2+1), and (Y2+3,X2+1).

[0073] The Y-coordinate value using the slope of a straight line refers to a Y-coordinate value based on the slope of a defined point that constitutes the clavicle line, for example, a point whose distance from the point being evaluated is within a threshold.

[0074] Furthermore, as described above, the clavicle extension means 323 performed the process of acquiring the lower clavicle line. However, the clavicle extension means 323 may also acquire the upper clavicle line using a similar algorithm.

[0075] The second clavicle line detection means 324 detects the opposite boundary point that satisfies the clavicle width condition for each point constituting the clavicle line (e.g., the inferior clavicle line) acquired by the clavicle extension means 323, and detects the clavicle line (e.g., the superior clavicle line) that includes the said boundary point.

[0076] The second clavicle line detection means 324 preferably detects the opposite boundary point that satisfies the clavicle width condition with respect to the lower clavicle line determined by the clavicle extension means 323, and then detects the upper clavicle line that includes said boundary point.

[0077] The clavicle width condition refers to the width (thickness) of the clavicle. Typically, the clavicle width condition is a range of width (thickness) of the clavicle. For example, the clavicle width condition might be 11 to 36 pixels.

[0078] The second clavicle line detection means 324 preferably detects the upper clavicle line using the algorithm described above. However, for the lower clavicle line, which has already been detected, it is preferable to detect two or more boundary candidate points on the opposite side that satisfy the clavicle width condition, and then perform the following processing on each of these two or more boundary candidate points to determine the pixels that constitute the upper clavicle line.

[0079] In other words, the second clavicle line detection means 324 calculates, for example, the signal difference map1 value and the signal difference map2 value for each of the two or more boundary candidate points. Next, the second clavicle line detection means 324 may, for example, select a point from among the two or more boundary candidate points where the signal difference map1 value satisfies a condition (e.g., the maximum) as a point constituting the upper clavicle line. Alternatively, the second clavicle line detection means 324 may, for example, select a point from among the two or more boundary candidate points where the signal difference map1 value satisfies a condition (e.g., above a threshold) and the signal difference map2 value satisfies a condition (e.g., the maximum) as a point constituting the upper clavicle line.

[0080] The costal line acquisition unit 33 analyzes the chest radiographic image and acquires the lower costal line, which is the lower boundary of the ribs, and the upper costal line, which is the upper boundary of the ribs. Note that analyzing the chest radiographic image may also be done by analyzing the images of the lung fields that make up the chest radiographic image.

[0081] The costal line acquisition unit 33 is preferably capable of acquiring the lower costal line and the upper costal line from the lung field detected by the lung field detection unit 31 from the chest transmission image.

[0082] When the rib cage acquisition unit 33 determines the pixels that constitute the lower rib cage and the pixels that constitute the upper rib cage, it is preferable to determine them starting from the pixels on the outside of the chest transmission image.

[0083] The rib line acquisition unit 33 preferably acquires the lower rib line and the upper rib line using, for example, the rib line acquisition means 331 and the correction means 332.

[0084] The costal line acquisition means 331 is one or more pixels that constitute the chest transmission image, and acquires an upper representative value and a lower representative value for each of the one or more pixels. Next, the costal line acquisition means 331 acquires a representative difference value, which is the difference between the upper representative value and the lower representative value, for each of the one or more pixels. It is preferable that the range of pixels that constitute the chest transmission image is the range of the lung field.

[0085] Next, the rib cage acquisition means 331 determines two or more pixels that satisfy the maximum condition among the representative difference values ​​of pixels with the same X coordinate value. The maximum condition is that the pixels correspond to the maximum value among the surrounding pixels with the same X coordinate value. The rib cage acquisition means 331 then designates the pixels corresponding to the maximum value as pixels that constitute the lower rib cage.

[0086] Furthermore, the rib line acquisition means 331 determines two or more pixels that satisfy the minimum condition among the representative difference values ​​of pixels with the same X coordinate value. The minimum condition is that the pixels correspond to the local minimum value among the surrounding pixels with the same X coordinate value. The rib line acquisition means 331 then designates the pixels corresponding to the local minimum value as pixels that constitute the upper rib line.

[0087] The rib line acquisition means 331 then shifts the search range in the X-axis direction by one pixel at a time, determining two or more pixels that satisfy the maximum condition. These pixels become the pixels that constitute the lower rib line. The rib line acquisition means 331 also shifts the search range in the X-axis direction by one pixel at a time, determining two or more pixels that satisfy the minimum condition. These pixels become the pixels that constitute the upper rib line.

[0088] As mentioned above, the rib cage acquisition means 331 calculates, for example, "representative difference value = upper representative value - lower representative value". The rib cage acquisition means 331 calculates, for example, "representative difference value (difference in weighted average values) = (upper weighted average value) - (lower weighted average value)" for each pixel of interest. Note that the larger the representative difference value, the larger the signal value (white area) above the pixel of interest, and conversely, the smaller the representative difference value, the larger the signal value (white area) below the pixel of interest.

[0089] The upper representative value is a value that indicates the trend of the pixel values ​​of pixels above the pixel of interest. For example, the upper representative value is the weighted average of the pixel values ​​of two or more pixels above the pixel of interest. For example, for a pixel of interest (x,y), the upper representative value is "Upper Representative Value = (Pixel Value (x,y-1) × 5 + Pixel Value (x,y-2) × 4 + Pixel Value (x,y-3) × 3 + Pixel Value (x,y-4) × 2 + Pixel Value (x,y-5) × 1) / (5 + 4 + 3 + 2 + 1)". In this calculation formula, the number of pixels used is 5, but other numbers such as 4, 3, or 6 may also be used. Furthermore, while it is preferable for this calculation formula to calculate a weighted average, it may also be a formula to calculate an average value.

[0090] The downward representative value (DOWN) is a value that indicates the trend of pixel values ​​of pixels below the pixel of interest. For example, the DOWN representative value is the weighted average of the pixel values ​​of two or more pixels below the pixel of interest. For example, for a pixel of interest (x,y), the DOWN representative value is calculated as follows: "DOWN representative value = (pixel value (x,y+1) × 5 + pixel value (x,y+2) × 4 + pixel value (x,y+3) × 3 + pixel value (x,y+4) × 2 + pixel value (x,y+5) × 1) / (5 + 4 + 3 + 2 + 1)". Note that the number of pixels used in this calculation is 5, but other numbers such as 4, 3, or 6 can also be used.

[0091] Furthermore, a pixel-by-pixel map constructed from the difference in the weighted average values ​​for each pixel is called a direction-dependent weighted average map. It can also be said that a direction-dependent weighted average map is a map that associates representative difference values ​​with each pixel.

[0092] The rib line acquisition means 331 uses angle information of rib extension corresponding to the location of the ribs to acquire upper candidate points constituting the upper rib line or lower candidate points constituting the lower rib line, acquires representative difference values ​​only for the pixels corresponding to the candidate points that are the upper candidate points or lower candidate points, and preferably adopts pixels whose representative difference values ​​satisfy the adoption conditions as pixels constituting the rib line that is the upper rib line or the lower rib line. Note that the adoption conditions here are maximum conditions or minimum conditions. In other words, the adoption condition for pixels constituting the lower rib line is that the representative difference value is a local maximum. The adoption condition for pixels constituting the upper rib line is that the representative difference value is a local minimum.

[0093] The angle information for rib extension is as follows: (1) The outer 30% of the ribs is "0 or -1", (2) The inner 70% of the ribs and the slope of the determined line is "-0.5 or less" is "0 or -1", (3) The inner 70% of the ribs and the slope of the determined line is "-0.5 to 0.5" is "-0, 3 or -0.8", and (4) The inner 70% of the ribs and the slope of the determined line is "0.5 or more" is "0 or 1".

[0094] Furthermore, using information on the angle of rib extension depending on the location of the ribs is equivalent to taking the shape of the ribs into consideration.

[0095] The correction means 332 determines whether two pixels that constitute the lower rib line and the upper rib line, respectively, acquired by the rib line acquisition means 331, satisfy the error condition.

[0096] If the pixel values ​​of two pixels satisfy the error condition, the correction means 332 searches for pixels in the Y direction and obtains an upper or lower rib line that includes a pixel with a pixel value that does not satisfy the error condition. The correction means 332 searches for pixels in the Y direction and identifies pixels with a pixel value that does not satisfy the error condition, is the pixel value of a neighboring pixel with a different X coordinate value, and whose difference from the pixel value of a pixel constituting the clavicle line is greater than or equal to a threshold as the pixel to be corrected. The correction means 332 then detects a pixel value that does not satisfy the error condition from the pixel values ​​of neighboring pixels with the same Y coordinate value as the pixel to be corrected, and determines that the pixel corresponding to that pixel value is a pixel constituting the upper or lower rib line.

[0097] An error condition is when the difference in pixel values ​​between two pixels at the same position in X is large enough to satisfy a predetermined condition. For example, an error condition is when the difference in pixel values ​​between two pixels at the same position in X is equal to or greater than a threshold. Two pixels at the same position in X are pixels that make up the same lower rib line (e.g., pixel (x,y)) and pixels that make up the same upper rib line (e.g., pixel (x,yn) (n is a natural number)). For example, an error condition is when the pixel values ​​of two pixels at the same position in X differ by more than twice the difference.

[0098] When the correction means 332 searches for pixels in the Y direction, it is preferable to search only a predetermined narrow range (for example, the range of the maximum width of the ribs) for pixels constituting the lower rib line or the upper rib line.

[0099] Output unit 4 outputs various types of information. These types of information include, for example, the lower clavicle line, the upper clavicle line, the lower costal line, and the upper costal line.

[0100] Here, "output" is a concept that includes display on a screen, projection using a projector, printing with a printer, sound output, transmission to an external device, storage on a recording medium, and transfer of processing results to other processing devices or other programs.

[0101] The clavicle line output unit 41 outputs the inferior clavicle line and superior clavicle line acquired by the clavicle line acquisition unit 32. It is preferable for the clavicle line output unit 41 to output the inferior clavicle line and superior clavicle line on top of the chest transmission image. It is preferable for the clavicle line output unit 41 to output the inferior clavicle line and superior clavicle line on top of the chest transmission image in a manner that clearly shows the lung area detected by the lung area detection unit 31. Outputting the inferior clavicle line and superior clavicle line means outputting them in a manner that clearly shows the inferior clavicle line and superior clavicle line.

[0102] The rib line output unit 42 outputs the lower rib line and upper rib line acquired by the rib line acquisition unit 33. It is preferable for the rib line output unit 42 to output the lower rib line and upper rib line on top of the chest transmission image. It is preferable for the rib line output unit 42 to output the lower rib line and upper rib line on top of the chest transmission image in a manner that clearly shows the lung area detected by the lung area detection unit 31. Note that outputting the lower rib line and upper rib line means outputting them in a manner that clearly shows the lower rib line and upper rib line.

[0103] The storage unit 1 and the image storage unit 11 are preferably made of non-volatile recording media, but can also be made of volatile recording media.

[0104] The process by which information is stored in the storage unit 1, etc. is not relevant. For example, information may be stored in the storage unit 1, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 1, etc., or information input via an input device may be stored in the storage unit 1, etc.

[0105] The reception unit 2 can be implemented using device drivers for input means such as touch panels and keyboards, or control software for menu screens.

[0106] The processing unit 3, lung field detection unit 31, clavicle line acquisition unit 32, costal line acquisition unit 33, candidate line detection means 321, partial line detection means 322, clavicle extension means 323, second clavicle line detection means 324, costal line acquisition means 331, and correction means 332 can typically be implemented using a processor, memory, etc. The processing procedures of the processing unit 3, etc., are typically implemented in software, and this software is recorded on a recording medium such as ROM. However, it may also be implemented in hardware (dedicated circuitry). The processor can be a CPU, MPU, GPU, etc., and the type is not limited.

[0107] Output unit 4, clavicle line output unit 41, and rib line output unit 42 may or may not be considered to include output devices such as displays and speakers. Output unit 4, etc., can be implemented using driver software for the output device, or driver software for the output device and the output device itself.

[0108] Next, an example of the operation of the bone structure contour extraction device A will be explained using the flowchart in Figure 2.

[0109] (Step S201) The lung area detection unit 31 reads the chest transmission image from the image storage unit 11. The lung area detection unit 31 analyzes the chest transmission image and detects the lung area.

[0110] (Step S202) The clavicle line acquisition unit 32 detects the clavicle line within the lung field detected by the lung field detection unit 31. An example of the process for detecting such a clavicle line (clavicular line acquisition process) will be explained using the flowchart in Figure 3.

[0111] The clavicle line acquisition unit 32 may also detect the clavicle line from the chest transparency image stored in the image storage unit 11.

[0112] (Step S203) The rib line acquisition unit 33 detects the rib lines within the lung field detected by the lung field detection unit 31. An example of the process for detecting such rib lines (rib line acquisition process) will be explained using the flowchart in Figure 6.

[0113] The rib cage line acquisition unit 33 may also detect rib cage lines from the chest transparency image stored in the image storage unit 11.

[0114] (Step S204) The processing unit 3 configures the output image. The processing unit 3 configures, for example, an image in which the clavicle line and costal line are clearly indicated within the chest transmission image. The processing unit 3 configures, for example, an image in which the lung fields, clavicle line, and costal line are clearly indicated within the chest transmission image.

[0115] (Step S205) The output unit 4 outputs the image formed in step S204. The process is modified.

[0116] In the flowchart shown in Figure 2, for example, processing begins when the reception unit 2 receives a start instruction.

[0117] Next, an example of the clavicle line acquisition process in step S202 will be explained using the flowchart in Figure 3.

[0118] (Step S301) The candidate line detection means 321 detects two or more candidate lines from the chest transmission image (image of the lung field region). For this detection, as described above, for example, the Hough transform is used.

[0119] (Step S302) The partial linear detection means 322 assigns 1 to counter i.

[0120] (Step S303) The partial line detection means 322 determines whether or not the i-th clavicle exists. If the i-th clavicle exists, the process proceeds to step S304; otherwise, it returns to the higher-level process. Note that i can usually be "1" or "2", because there are usually two clavicles.

[0121] (Step S304) The partial line detection means 322 detects the pixel with the highest pixel value among the pixels in the chest transmission image that match the clavicle line region condition. Such a pixel will be appropriately referred to as the highest value pixel.

[0122] (Step S305) The partial linear detection means 322 assigns 1 to counter j.

[0123] (Step S306) The partial line detection means 322 determines whether or not a j-th candidate line exists within the range that matches the clavicle line region condition. If a j-th candidate line exists, the process proceeds to step S307; if a j-th candidate line does not exist, the process proceeds to step S311.

[0124] (Step S307) The partial line detection means 322 obtains the slope of the j-th candidate line. Note that the technique for obtaining the slope of a line is a known technique.

[0125] (Step S308) The partial straight line detection means 322 determines whether the slope obtained in step S307 matches the clavicle inclination condition. If it matches the clavicle inclination condition, the process proceeds to step S309; ​​otherwise, the process proceeds to step S310.

[0126] (Step S309) The partial line detection means 322 obtains the distance between the highest value pixel and the j-th candidate line.

[0127] (Step S310) The partial linear detection means 322 increments the counter j by 1. Return to step S306.

[0128] (Step S311) The partial straight line detection means 322 determines the candidate straight line with the shortest distance obtained in step S309 as the partial straight line that constitutes the clavicle straight line.

[0129] (Step S312) The clavicle extension means 323 performs a clavicle extension process, which is the process of extending the partial straight line obtained in step S311. An example of the clavicle extension process will be explained using the flowchart in Figure 4.

[0130] (Step S313) The second clavicle line detection means 324 performs the second clavicle line detection process. An example of the second clavicle line detection process will be explained using the flowchart in Figure 5.

[0131] The second clavicle line detection process is a process that detects the superior or inferior clavicle line corresponding to the detected inferior or superior clavicle line. Since it is preferable to detect the inferior clavicle line first, it is preferable that the second clavicle line detection process is a process that detects the superior clavicle line corresponding to the detected inferior clavicle line.

[0132] (Step S314) The partial linear detection means 322 increments the counter i by 1. Return to step S303.

[0133] Next, an example of the clavicle extension process in step S312 will be explained using the flowchart in Figure 4.

[0134] (Step S401) The clavicle extension means 323 assigns 1 to counter i.

[0135] (Step S402) The clavicle extension means 323 determines whether there is an X-coordinate value that extends one more to the right from the right endpoint of the partial line (the initial reference point) or the modified reference point. If there is an X-coordinate value that extends to the right from the reference point, the process goes to step S403; otherwise, the process goes to step S415. The initial reference point is the endpoint of the partial line. The reference point is modified in step S413. The reference point is a point that constitutes the clavicle line. The X-coordinate value that extends to the right from the reference point is the point with an X-coordinate value of "x+1" relative to the reference point (x, y). If there is an X-coordinate value that extends to the right from the reference point, it means that the clavicle line is extended by one pixel.

[0136] (Step S403) The clavicle extension means 323 acquires two or more candidate points with the same X coordinate value relative to the reference point (x, y). The two or more candidate points are (x+1, y), (x+1, y+1), and (x+1, y-1). It is preferable for the clavicle extension means 323 to acquire two or more candidate points while considering the clavicle inclination condition (slope of the partial straight line). The X coordinate value of each of the two or more candidate points is the same.

[0137] (Step S404) The clavicle extension means 323 substitutes 1 for counter j.

[0138] (Step S405) The clavicle extension means 323 determines whether or not the j-th candidate point exists among the candidate points obtained in step S403. If the j-th candidate point exists, the process proceeds to step S406; otherwise, the process proceeds to step S412.

[0139] (Step S406) The clavicle extension means 323 obtains the signal difference map1 value of the j-th candidate point.

[0140] (Step S407) The clavicle extension means 323 obtains the signal difference map2 value of the j-th candidate point.

[0141] (Step S408) The clavicle extension means 323 determines whether the signal difference map1 value obtained in step S406 and the signal difference map2 value obtained in step S407 satisfy the conditions. If the conditions are met, proceed to step S409; otherwise, proceed to step S411.

[0142] These conditions can also be called acceptance criteria. In this context, the acceptance criteria are, for example, that the signal difference map1 value is equal to or greater than a threshold (e.g., 7), and the signal difference map2 value is equal to or greater than a threshold (e.g., 0).

[0143] (Step S409) The clavicle extension means 323 obtains a Y coordinate value using the slope of the straight line.

[0144] (Step S410) The clavicle extension means 323 obtains the maximum value (α) of the signal difference map1 values ​​of the number of pixels corresponding to the upper threshold and the number of pixels corresponding to the lower threshold. Next, the clavicle extension means 323 obtains the Y coordinate value of the j-th candidate point. Then, the clavicle extension means 323 calculates a score using the Y coordinate value obtained from the slope of the line, the Y coordinate value of the j-th candidate point, the signal difference map1 value of the j-th candidate point, and α. The clavicle extension means 323 calculates a score using, for example, the formula "Score = {α - value of signal difference map1}" 2 +{Y coordinate value of reference candidate point - Y coordinate value of candidate point} 2 The score is calculated based on the following:

[0145] (Step S411) The clavicle extension means 323 increments counter j by 1. Return to step S405.

[0146] (Step S412) The clavicle extension means 323 determines the candidate point with the smallest score calculated in step S410 as a point that constitutes the clavicle line.

[0147] (Step S413) The clavicle extension means 323 uses the candidate point determined in step S412 as a reference point.

[0148] (Step S414) The clavicle extension means 323 increments counter i by 1. Return to step S402 or S416.

[0149] (Step S415) The clavicle extension means 323 assigns 1 to counter i.

[0150] (Step S416) The clavicle extension means 323 determines whether there is an X coordinate value that extends one more to the left with respect to the left endpoint of the partial straight line or the modified reference point. If there is an X coordinate value that extends one more to the left, the process goes to step S403; otherwise, it returns to the higher-level process.

[0151] Next, an example of the second clavicle line detection process in step S313 will be explained using the flowchart in Figure 5. In the flowchart in Figure 5, the explanation of steps identical to those in the flowchart in Figure 4 will be omitted.

[0152] (Step S501) The second clavicle line detection means 324 assigns 1 to counter i.

[0153] (Step S502) The second clavicle line detection means 324 determines whether or not there is an i-th point that constitutes one of the clavicle lines (for example, the lower clavicle line) acquired by the clavicle line acquisition unit 32. If there is an i-th point, the process proceeds to step S503; otherwise, the process returns to the higher level.

[0154] (Step S503) The second clavicle line detection means 324 determines two or more candidate points that have the same X coordinate value (x) as the i-th point (x, y) and that have a Y coordinate value that satisfies the clavicle width condition for the i-th point (x, y). Proceed to step S404.

[0155] In the flowchart of Figure 5, the process returns from step S414 to step S502.

[0156] Furthermore, in the flowchart of Figure 5, the second clavicle line detection means 324 may determine the pixels constituting the clavicle line from the candidate points using a different algorithm instead of steps S404 to S414. For example, the second clavicle line detection means 324 may determine the pixel with the smallest signal difference map2 value among two or more candidate points as the pixel constituting the upper clavicle line.

[0157] Next, an example of the rib cage line acquisition process in step S203 will be explained using the flowchart in Figure 6.

[0158] (Step S601) The rib line acquisition means 331 assigns 1 to counter i.

[0159] (Step S602) The rib line acquisition means 331 determines whether or not the i-th pixel exists within the range where pixels constituting the clavicle line may exist. If the i-th pixel exists, proceed to step S603; otherwise, proceed to step S607.

[0160] The areas in which pixels constituting the clavicle line may exist include, for example, the lung field, the lower threshold region within the lung field (e.g., 90%), and the entire chest radiographic image.

[0161] (Step S603) The rib line acquisition means 331 acquires the upper representative value of the i-th pixel.

[0162] (Step S604) The rib line acquisition means 331 acquires the lower representative value of the i-th pixel.

[0163] (Step S605) The rib line acquisition means 331 calculates a representative difference value, which is the difference between the upper representative value obtained in step S603 and the lower representative value obtained in step S604.

[0164] (Step S606) The rib line acquisition means 331 increments the counter i by 1. Return to step S602.

[0165] (Step S607) The rib line acquisition means 331 determines two or more pixels whose representative difference value of the same X coordinate value satisfies the maximum condition. Each of these two or more pixels is a different pixel that constitutes the lower rib line of the ribs. The maximum condition is that the pixels have the same X coordinate value and are pixels whose pixel value takes a maximum value.

[0166] (Step S608) The rib line acquisition means 331 assigns 1 to counter j.

[0167] (Step S609) The rib line acquisition means 331 determines whether or not the j-th pixel exists among the pixels determined in step S607. If the j-th pixel exists, proceed to step S610; otherwise, proceed to step S612.

[0168] (Step S610) The rib line acquisition means 331 performs a lower rib line extension process, which is a process of extending the lower rib line starting from the j-th pixel. An example of the lower rib line extension process will be explained using the flowchart in Figure 7. The lower rib line extension process is a process of extending the lower rib line starting from the pixel determined in step S607.

[0169] (Step S611) The rib line acquisition means 331 increments counter j by 1. Return to step S609.

[0170] (Step S612) The rib line acquisition means 331 determines two or more pixels whose representative difference value with the same Y coordinate value satisfies the minimum condition. Each of these two or more pixels is a different pixel that constitutes the upper rib line of the rib. The minimum condition is that the pixels have the same X coordinate value and are pixels whose pixel value takes a local minimum value.

[0171] (Step S613) The rib line acquisition means 331 assigns 1 to counter j.

[0172] (Step S614) The rib line acquisition means 331 determines whether or not the j-th pixel exists among the pixels determined in step S612. If the j-th pixel exists, proceed to step S615; otherwise, proceed to step S617.

[0173] (Step S615) The rib line acquisition means 331 performs an upper rib line extension process. An example of the upper rib line extension process will be explained using the flowchart in Figure 8. The upper rib line extension process is a process that extends the upper rib line starting from the j-th pixel.

[0174] (Step S616) The rib line acquisition means 331 increments counter j by 1. Return to step S614.

[0175] (Step S617) The correction means 332 performs a correction process. An example of the correction process will be explained using the flowchart in Figure 9.

[0176] (Step S618) The rib line acquisition means 331 determines whether or not to terminate the rib line acquisition process. If it terminates, it returns to the higher-level process; otherwise, it proceeds to step S607. The rib line acquisition means 331 determines to terminate the process, for example, after it has completed detecting the contour lines of the right ribs and the left ribs.

[0177] Next, an example of the lower rib line extension process in step S610 will be explained using the flowchart in Figure 7.

[0178] (Step S701) The rib line acquisition means 331 assigns 1 to counter i.

[0179] (Step S702) The rib line acquisition means 331 determines whether or not there is an i-th X coordinate value to which the rib line should be extended. If an i-th X coordinate value exists, the process proceeds to step S703; otherwise, it returns to the higher-level process.

[0180] Furthermore, the X-coordinate values ​​for which the costal line should be extended should, for example, not be at the edge of the lung field, not be at the edge of the chest radiographic image, and be below the upper threshold (e.g., 20%) and above the lower threshold (e.g., 90%) of the chest radiographic image.

[0181] (Step S703) The rib line acquisition means 331 acquires angle information for the location of the pixel with the i-th X coordinate value.

[0182] (Step S704) The rib line acquisition means 331 determines two or more downward candidate points using the angle information acquired in step S703.

[0183] (Step S705) The rib line acquisition means 331 assigns 1 to counter j.

[0184] (Step S706) The rib line acquisition means 331 determines whether or not the j-th lower candidate point exists among the lower candidate points determined in step S704. If the j-th lower candidate point exists, the process proceeds to step S707; otherwise, the process proceeds to step S709.

[0185] (Step S707) The rib line acquisition means 331 acquires a representative difference value of the j-th lower candidate point.

[0186] (Step S708) The rib line acquisition means 331 increments counter j by 1. Return to step S706.

[0187] (Step S709) The rib line acquisition means 331 determines the pixel corresponding to the largest representative difference value among the representative difference values ​​acquired in step S707 to be a pixel that constitutes the lower rib line. The pixel corresponding to the largest representative difference value is the pixel that satisfies the maximum condition.

[0188] (Step S710) The rib line acquisition means 331 changes the reference pixel to the pixel determined in step S709.

[0189] (Step S711) The rib line acquisition means 331 increments the counter i by 1. Return to step S702.

[0190] Next, an example of the upper costal line extension process in step S615 will be explained using the flowchart in Figure 8.

[0191] (Step S801) The rib line acquisition means 331 assigns 1 to counter i.

[0192] (Step S802) The rib line acquisition means 331 determines whether or not there is an i-th X coordinate value to which the rib line should be extended. If an i-th X coordinate value exists, the process proceeds to step S803; otherwise, it returns to the higher-level process.

[0193] (Step S803) The rib line acquisition means 331 acquires angle information for the location of the pixel with the i-th X coordinate value.

[0194] (Step S804) The rib line acquisition means 331 determines two or more upper candidate points using the angle information acquired in step S803.

[0195] (Step S805) The rib line acquisition means 331 assigns 1 to counter j.

[0196] (Step S806) The rib line acquisition means 331 determines whether or not the j-th upper candidate point exists among the upper candidate points determined in step S804. If the j-th upper candidate point exists, the process proceeds to step S807; otherwise, the process proceeds to step S809.

[0197] (Step S807) The rib line acquisition means 331 acquires the representative difference value of the j-th upper candidate point.

[0198] (Step S808) The rib line acquisition means 331 increments counter j by 1. Return to step S806.

[0199] (Step S809) The rib line acquisition means 331 determines the pixel corresponding to the smallest representative difference value among the representative difference values ​​acquired in step S807 to be a pixel that constitutes the upper rib line. The pixel corresponding to the smallest representative difference value is a pixel that satisfies the minimum condition.

[0200] (Step S810) The rib line acquisition means 331 changes the reference pixel to the pixel determined in step S809.

[0201] (Step S811) The rib line acquisition means 331 increments the counter i by 1. Return to step S802.

[0202] In addition, since the lower rib line is determined in the flowchart of Figure 8, the rib line acquisition means 331 may acquire the upper rib line using other algorithms, such as determining two or more candidate points that satisfy the rib width condition from each pixel constituting the lower rib line, and determining the pixel with the smallest representative difference value among these two or more candidate points to be the pixel constituting the upper rib line.

[0203] Next, an example of the correction process in step S617 will be explained using the flowchart in Figure 9.

[0204] (Step S901) The correction means 332 assigns 1 to counter i.

[0205] (Step S902) The correction means 332 determines whether or not the i-th rib exists. If the i-th rib exists, the process proceeds to step S903; otherwise, it returns to the higher-level process.

[0206] (Step S903) The correction means 332 assigns 1 to counter j.

[0207] (Step S904) The correction means 332 determines whether the j-th X-coordinate value exists within the lower or upper costal line of the i-th rib. If the j-th X-coordinate value exists, proceed to step S905; otherwise, proceed to step S911.

[0208] (Step S905) The correction means 332 obtains the pixel value of the pixel corresponding to the j-th X coordinate value of the upper rib line.

[0209] (Step S906) The correction means 332 obtains the pixel value of the pixel corresponding to the j-th X coordinate value of the lower rib line.

[0210] (Step S907) The correction means 332 determines whether the pixel value acquired in step S905 and the pixel value acquired in step S906 satisfy the error condition. If the error condition is met, the process proceeds to step S908; otherwise, the process proceeds to step S910. The error condition is, for example, that the difference between the pixel value acquired in step S905 and the pixel value acquired in step S906 is 2 times or more.

[0211] (Step S908) The correction means 332 determines an inappropriate pixel value (the pixel value with the larger difference) by comparing the pixel value of the pixel that constitutes the rib line with the surrounding pixel value from among the pixel values ​​acquired in step S905 and the pixel values ​​acquired in step S906. The correction means 332 determines two or more candidate points that are above or below the pixel corresponding to the inappropriate pixel value.

[0212] (Step S909) The correction means 332 determines, among the two or more candidate pixel values ​​determined in step S908, a pixel value that does not satisfy the error condition for one of the pixel values. Next, the correction means 332 determines that the pixel having that pixel value is a pixel that constitutes the rib line. If there are multiple pixel values ​​that do not satisfy the error condition, the correction means 332 determines that the pixel that is closer in distance from the pixel corresponding to the inappropriate pixel value determined in step S908 is a pixel that constitutes the rib line.

[0213] (Step S910) The correction means 332 increments the counter j by 1. Return to step S904.

[0214] (Step S911) The correction means 332 increments the counter i by 1. Return to step S902.

[0215] The following describes a specific example of the operation of the bone structure contour extraction device A in this embodiment. For example, let's assume that the chest X-ray image (an example of a chest radiographic image) shown in Figure 10(a) is stored in the image storage unit 11.

[0216] Then, the clavicle line acquisition unit 32 of the bone structure contour extraction device A detects the inferior clavicle line (1001) and the superior costal line (1002) using the algorithm described above. In addition, the costal line acquisition unit 33 detects the inferior costal line (1003) and the superior costal line (1004) using the algorithm described above.

[0217] The rib line acquisition unit 33, for example as shown in Figure 11, constructs a map consisting of representative difference values ​​for each pixel (here referred to as a "direction-dependent weighted average map"), and constructs the lower rib line (1101) from the pixels that satisfy the maximum condition from among the representative difference values ​​on the map, and constructs the upper rib line (1102) from the pixels that satisfy the minimum condition. The rib line acquisition unit 33 then performs the correction process described above and acquires the lower rib line (1101) and the upper rib line (1102) using the algorithm described above.

[0218] Next, the processing unit 3 constructs an image on top of the chest X-ray image (Figure 10) in which the inferior clavicle line (1001), superior costal line (1002), inferior costal line (1003), and superior costal line (1004) are revealed. Figure 11(b) is a map (direction-dependent weighted average map) of the representative difference values ​​of each pixel in a portion of the chest X-ray image 1103 in Figure 11(a).

[0219] Next, the output unit 4 outputs the image. An example of such an image is shown in Figure 10(b).

[0220] As described above, according to this embodiment, the clavicle can be appropriately detected from a transcatheter chest image. In particular, the clavicle can be appropriately detected from a transcatheter chest image without obtaining a large amount of training data based on transcatheter chest images.

[0221] Furthermore, according to this embodiment, ribs can be appropriately detected from chest transmission images. In particular, ribs can be appropriately detected from chest transmission images without obtaining a large amount of training data based on chest transmission images.

[0222] Furthermore, according to this embodiment, it is possible to output an image that clearly shows the bones in relation to a transmissive image of the chest.

[0223] Furthermore, according to this embodiment, after detecting the lung area from the chest transmission image, a process is performed to detect the bone area within that lung area, enabling high-speed detection of the bone area. In this case, the bone area refers to one or more bones, such as the clavicle or ribs.

[0224] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements the bone structure contour extraction device A in this embodiment is the following program. In other words, this program causes a computer that can access an image storage unit where a chest transparency image is stored to function as a clavicle line acquisition unit that acquires the lower clavicle line, which is the lower boundary line of the clavicle, and the upper clavicle line, which is the upper boundary line of the clavicle, using the chest transparency image, and a clavicle line output unit that outputs the lower clavicle line and the upper clavicle line. The clavicle line acquisition unit includes a candidate line detection means that analyzes the chest transparency image and detects one or more candidate lines that are candidates for the boundary line of the clavicle, and a drawing of the range of the one or more candidate lines that matches the clavicle inclination condition and matches the clavicle line region condition in the chest transparency image. This is a program for causing the computer to function, comprising: partial line detection means for detecting the pixel with the highest pixel value among the elements and detecting the candidate line closest to that pixel as a partial line constituting the lower clavicle line or the upper clavicle line; clavicle extension means for detecting the lower clavicle line or the upper clavicle line extended from the endpoint of the partial line; and second clavicle line detection means for detecting the opposite boundary point that satisfies the clavicle width condition for each point constituting the lower clavicle line or the upper clavicle line and detecting the upper clavicle line or the lower clavicle line including the boundary point.

[0225] Furthermore, the software that realizes the bone structure contour extraction device A in this embodiment is the following program. In other words, this program is a program that causes a computer that can access an image storage unit in which a chest transparency image is stored to function as a rib line acquisition unit that analyzes the chest transparency image and acquires the lower rib line, which is the lower boundary line of the ribs, and the upper rib line, which is the upper boundary line of the ribs, and a rib line output unit that outputs the lower rib line and the upper rib line. The rib line acquisition unit is a program that causes the computer to function as a rib line acquisition means that, for each of the one or more pixels that constitute the chest transparency image and exist within a predetermined width, acquires the difference between an upper representative value, which is a representative value of the pixel values ​​of two or more pixels above the pixel, and a lower representative value, which is a representative value of the pixel values ​​of two or more pixels below the pixel, and the pixel with the largest or smallest difference is the pixel that constitutes the lower rib line, and the pixel with the smallest or largest difference is the pixel that constitutes the upper rib line, and acquires the lower rib line and the upper rib line.

[0226] (Embodiment 2) In this embodiment, an image processing device will be described that uses information about the bone contour, which is the output of the bone structure contour extraction device A, to acquire a bone transparency image, which is an image with the bone removed, and outputs the bone transparency image.

[0227] Figure 12 is a block diagram of the image processing device B in this embodiment. The image processing device B comprises a receiving unit 2, a storage unit 5, a processing unit 6, and an output unit 7. The storage unit 5 comprises an image storage unit 11, a clavicle line storage unit 51, and a costal line storage unit 52. The processing unit 6 comprises a lung field detection unit 31, a clavicle line acquisition unit 32, a costal line acquisition unit 33, a bone transparency unit 61, and a lesion detection unit 62. The bone transparency unit 61 comprises a pixel value correction means 611. The output unit 7 comprises a bone transparency image output unit 71 and a lesion output unit 72.

[0228] The storage unit 5 stores various types of information. These types of information include, for example, information identifying pixels that make up the inferior clavicle line, information identifying pixels that make up the superior clavicle line, information identifying pixels that make up the inferior costal line, information identifying pixels that make up the superior costal line, a translucent chest image, angle information, and various conditions described later.

[0229] The information that identifies the pixels constituting the inferior clavicle line will be referred to as the inferior clavicle line as appropriate. The information that identifies the pixels constituting the superior clavicle line will be referred to as the superior clavicle line as appropriate. The information that identifies the pixels constituting the inferior costal line will be referred to as the inferior costal line as appropriate. The information that identifies the pixels constituting the superior costal line will be referred to as the superior costal line as appropriate.

[0230] The clavicle line storage unit 51 stores information that identifies pixels constituting the lower clavicle line and information that identifies pixels constituting the upper clavicle line. The information that identifies the pixels is, for example, coordinate values ​​(x,y). Preferably, the information in the clavicle line storage unit 51 is information passed from the clavicle line output unit 41. Preferably, the information in the clavicle line storage unit 51 is information acquired by the clavicle line acquisition unit 32.

[0231] The rib line storage unit 52 stores information identifying pixels that constitute the lower rib line and information identifying pixels that constitute the upper rib line. Preferably, the information in the rib line storage unit 52 is information passed from the rib line output unit 42. Preferably, the information in the clavicle line storage unit 51 is information acquired by the rib line acquisition unit 33.

[0232] The processing unit 6 performs various processes. These processes include, for example, those performed by the lung field detection unit 31, the clavicle line acquisition unit 32, the costal line acquisition unit 33, and the bone transparency unit 61.

[0233] The bone-transmitting unit 61 acquires a bone-transmitting image of the chest that has passed through the bone. The bone-transmitting unit 61 acquires a bone-transmitting image of the chest that has passed through the ribs. The bone-transmitting unit 61 may also acquire a bone-transmitting image of the chest that has passed through the clavicle.

[0234] The bone-transparent portion 61 corrects the pixel value of the rib pixel, which is the pixel between the lower costal line and the upper costal line corresponding to the lower costal line, using a correction amount, obtains the corrected pixel value, sets this corrected pixel value as the pixel value of the rib pixel, and obtains a bone-transparent image in which the ribs are transparent to the chest-transparent image.

[0235] The correction amount is information regarding the difference between the internal representative value and the external representative value. Here, the internal representative value is the representative value of the pixel values ​​of two or more pixels on the inside of the rib. Here, the external representative value is the representative value of the pixel values ​​of two or more pixels on the outside of the rib.

[0236] Two or more pixels on the inside of a rib are one or more pixels below (for example, (x,y+1)) each of the two or more pixels ((x,y)) that make up the upper costal line. Two or more pixels on the inside of a rib are one or more pixels above (for example, (x,y-1)) each of the two or more pixels ((x,y)) that make up the lower costal line.

[0237] Two or more pixels on the outside of a rib are one or more pixels above (e.g., (x,y-1)) each of the two or more pixels ((x,y)) that make up the upper costal line. Two or more pixels on the inside of a rib are one or more pixels below (e.g., (x,y+1)) each of the two or more pixels ((x,y)) that make up the lower costal line.

[0238] The representative value is, for example, the average of the pixel values ​​of two or more target points, the median of the pixel values ​​of two or more target points, or the weighted average of the pixel values ​​of two or more target points. The information regarding the difference is preferably the difference between the inner representative value and the outer representative value, but it may also be, for example, the square of the difference between the inner representative value and the outer representative value. The target points are two or more pixels on the inside of the ribs or two or more pixels on the outside of the ribs.

[0239] The bone-transmitting portion 61 corrects the pixel values ​​of the clavicle pixels using a correction amount, obtains the corrected pixel values, uses the corrected pixel values ​​as the pixel values ​​of the clavicle pixels, and obtains a bone-transmitting image that has passed through the clavicle to the chest-transmitting image. Note that the clavicle pixels are pixels inside the clavicle.

[0240] The correction amount is information regarding the difference between the medial representative value and the lateral representative value. Here, the medial representative value is the representative value of the pixel values ​​of two or more pixels on the medial side of the clavicle. Here, the lateral representative value is the representative value of the pixel values ​​of two or more pixels on the lateral side of the clavicle. Representative values ​​are, for example, the average value of the pixel values ​​of two or more target points, the median value of the pixel values ​​of two or more target points, or the weighted average value of the pixel values ​​of two or more target points.

[0241] The above correction amount can also be called the "transparency processing parameter." For example, the bone transparency area 61 uses "pixel value of the rib before processing - transparency processing parameter" as the corrected pixel value. Similarly, the bone transparency area 61 uses "pixel value of the clavicle before processing - transparency processing parameter" as the corrected pixel value.

[0242] The pixel value correction means 611 corrects the corrected pixel value obtained by the bone transparency unit 61 using the other two or more corrected pixel values ​​if the corrected pixel value obtained by the bone transparency unit 61 satisfies an abnormal condition when compared with two or more other corrected pixel values.

[0243] The other two or more corrected pixel values ​​are the corrected pixel values ​​of pixels with the same X coordinate value as the pixel corresponding to the corrected pixel value acquired by the bone transparency unit 61.

[0244] An abnormal condition is, for example, when the difference between the corrected pixel value and two or more other corrected pixel values ​​is greater than or equal to a threshold. An abnormal condition is, for example, "corrected pixel value >= mean of two or more other corrected pixel values ​​+ 0.5 × standard deviation of two or more other corrected pixel values". Note that 0.5 × standard deviation of two or more other corrected pixel values ​​is expressed as "0.5SD" as appropriate.

[0245] The pixel value correction means 611, for example, if the corrected pixel value satisfies an abnormal condition, changes the corrected pixel value so that the corrected pixel value no longer satisfies the abnormal condition. The pixel value correction means 611, for example, if the corrected pixel value satisfies an abnormal condition, changes the corrected pixel value to a representative value of one or more pixels outside the bone (lung field) that have the same Y coordinate value as the pixel corresponding to the corrected pixel value (for example, the average value of the pixel values ​​of two pixels outside the bone contour).

[0246] The pixel value correction means 611, for example, linearly regresses the pixel values ​​of the upper and lower ends of the ribs in the y direction after correction, and if a pixel value showing 0.5SD or more of the internal pixel value is detected within, for example, 25% of the thickness of the ribs from the contour, it sets that pixel value to the value after linear regression + 0.5SD.

[0247] The lesion detection unit 62 detects lesions using the bone radiolucency image acquired by the processing of the bone radiolucency unit 61. Since the technique for detecting lesions is publicly known, a detailed explanation is omitted.

[0248] The output unit 7 outputs various types of information. These types of information include, for example, radioluminescent images of bone and radioluminescent images that clearly indicate the location of lesions.

[0249] Here, "output" is a concept that includes display on a screen, projection using a projector, printing with a printer, sound output, transmission to an external device, storage on a recording medium, and transfer of processing results to other processing devices or other programs.

[0250] The bone radiolucency image output unit 71 outputs the bone radiolucency image acquired by the bone radiolucency unit 61. It is preferable that the bone radiolucency image output unit 71 can output a bone radiolucency image that clearly indicates the location of the lesion.

[0251] The lesion output unit 72 outputs the location of the lesion corresponding to the bone radiolucent image. The location of the lesion is the location detected by the lesion detection unit 62.

[0252] The storage section 5 and the rib cage storage section 52 are preferably made of non-volatile recording media, but can also be made of volatile recording media.

[0253] The process by which information is stored in the storage unit 5, etc. is not relevant. For example, information may be stored in the storage unit 5, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 5, etc., or information input via an input device may be stored in the storage unit 5, etc.

[0254] The processing unit 6, bone transparency unit 61, pixel value correction means 611, and lesion detection unit 62 can typically be implemented using a processor, memory, etc. The processing procedures of the processing unit 6, etc., are usually implemented in software, and this software is recorded on a recording medium such as ROM. However, it may also be implemented in hardware (dedicated circuitry). The processor can be a CPU, MPU, GPU, etc., and the type is not limited.

[0255] The output unit 7, the bone radiolucency image output unit 71, and the lesion output unit 72 may or may not be considered to include output devices such as displays and speakers. The output unit 7, etc., can be implemented using driver software for the output device, or driver software for the output device and the output device itself.

[0256] Next, an example of the operation of the image processing device B will be explained using the flowchart in Figure 13. In the flowchart in Figure 13, the explanation for the same steps as in the flowchart in Figure 2 will be omitted.

[0257] (Step S1301) The bone-translucent portion 61 undergoes bone-translucency treatment. An example of bone-translucency treatment will be explained using the flowchart in Figure 14.

[0258] (Step S1302) The lesion detection unit 62 detects lesions in the bone radiolucency image acquired in step S1301.

[0259] (Step S1303) The processing unit 6 constructs an output image that clearly indicates the area of ​​the lesion detected in step S1302 with respect to the bone radiolucency image acquired in step S1301.

[0260] (Step S1304) The output unit 7 outputs the image formed in step S1303. The process ends.

[0261] Next, an example of bone transparency treatment in step S1301 will be explained using the flowchart in Figure 14.

[0262] (Step S1401) The bone-transparent portion 61 assigns 1 to counter i.

[0263] (Step S1402) The bone-transmitting portion 61 determines whether or not the i-th bone to be transmitted is present. If the i-th bone is present, proceed to step S1403; if the i-th bone is not present, proceed to step S1405.

[0264] Furthermore, the bone-transparent portion 61 determines whether or not the i-th bone exists using the costal line information detected in step S203, or the costal line information detected in step S203 and the clavicle line information detected in step S202.

[0265] (Step S1403) The bone-transmitting unit 61 performs a translucency process on the region of the i-th bone. This process is called single-bone translucency processing. An example of single-bone translucency processing will be explained using the flowchart in Figure 15.

[0266] (Step S1404) The bone-translucent portion 61 increments the counter i by 1. Return to step S1402.

[0267] (Step S1405) The bone-transparent portion 61 substitutes 1 for counter i.

[0268] (Step S1406) The bone-transparent portion 61 determines whether or not the i-th bone region exists. If the i-th bone region exists, the process proceeds to step S1407; otherwise, it returns to the higher-level process.

[0269] The bone-transparent portion 61, for example, uses the information on the rib line detected in step S203, or the information on the rib line detected in step S203 and the information on the clavicle line detected in step S202, to determine whether or not the region of the i-th bone exists.

[0270] (Step S1407) The pixel value correction means 611 performs bone margin influence correction processing. An example of bone margin influence correction processing will be explained using the flowchart in Figure 16.

[0271] The bone edge influence correction process is a process for correcting pixel values that is performed because the single bone penetration process performed in step S1403 may be overcorrected.

[0272] (Step S1408) The bone penetration unit 61 increments the counter i by 1 and returns to step S1406.

[0273] In the flowchart of FIG. 14, it is preferable to perform the bone edge influence correction process in step S1407, but it may not be performed.

[0274] Next, an example of the single bone penetration process in step S1403 will be described using the flowchart of FIG. 15.

[0275] (Step S1501) The bone penetration unit 61 acquires information for specifying pixels that constitute the upper line of the bone. The upper line of the bone is the upper rib line or the upper clavicle line. The information for specifying the pixels that constitute the upper line of the bone is, for example, a set of coordinate values (x, y) indicating the positions of the pixels that constitute the upper line of the bone.

[0276] (Step S1502) The bone penetration unit 61 acquires all the pixel values of the pixels inside the bone within the threshold of each of two or more pixels that constitute the upper line of the bone. The threshold is preferably "1", but may be 2 or more. Also, the pixels inside the bone within the threshold of each of two or more pixels are pixels corresponding to the same X coordinate value (x) as each of the two or more pixels (x, y), and are pixels inside the bone from the pixels (x, y).

[0277] (Step S1503) The bone penetration unit 61 acquires an inner representative value that is the representative value of all the pixel values acquired in step S1502. The inner representative value is preferably, for example, the average value of the pixel values acquired in step S1502, but may also be the median value, weighted average value, etc.

[0278] (Step S1504) The bone penetration unit 61 acquires all the pixel values of the pixels outside the bone within the threshold values of two or more pixels constituting the upper line of the bone. Note that the threshold value is preferably "1", but may also be two or more. Further, the pixels outside the bone within the threshold values of two or more pixels are pixels corresponding to the same X coordinate value (x) as the two or more pixels (x, y), and are pixels outside the bone from the pixels (x, y).

[0279] (Step S1505) The bone penetration unit 61 acquires an outer representative value that is the representative value of all the pixel values acquired in Step S1504. Note that the outer representative value is preferably, for example, the average value of the pixel values acquired in Step S1504, but may also be the median value, weighted average value, etc.

[0280] (Step S1506) The bone penetration unit 61 acquires an upper correction amount that is a correction amount regarding the difference between the inner representative value acquired in Step S1503 and the outer representative value acquired in Step S1505. Note that, for example, "upper correction amount = inner representative value - outer representative value".

[0281] (Step S1507) The bone penetration unit 61 acquires information (for example, a set of coordinate values (x, y) indicating the positions of pixels) for specifying the pixels constituting the lower line of the bone. Note that the lower line of the bone is the lower rib line or the lower clavicle line.

[0282] (Step S1508) The bone penetration unit 61 acquires all the pixel values of the pixels inside the bone within the threshold values of two or more pixels constituting the lower line of the bone. Note that the threshold value is preferably "1", but may also be two or more.

[0283] (Step S1509) The bone penetration unit 61 acquires an inner representative value that is the representative value of all the pixel values acquired in Step S1508. Note that the inner representative value is, for example, the average value, median value, weighted average value of the pixel values acquired in Step S1508.

[0284] (Step S1510) The bone-transmitting unit 61 acquires the pixel values ​​of all pixels outside the bone that are within the threshold of each pixel constituting the lower line of the bone. The threshold is preferably "1", but may be 2 or higher.

[0285] (Step S1511) The bone-translucent portion 61 obtains an outer representative value, which is a representative value of all the pixel values ​​obtained in step S1510. The outer representative value is, for example, the mean, median, and weighted mean of the pixel values ​​obtained in step S1510.

[0286] (Step S1512) The bone-translucent portion 61 obtains a downward correction amount, which is a correction amount for the difference between the medial representative value obtained in step S1509 and the lateral representative value obtained in step S1511. For example, "downward correction amount = medial representative value - lateral representative value".

[0287] (Step S1513) The bone-transparent portion 61 assigns 1 to counter i.

[0288] (Step S1514) The bone-transparent section 61 determines whether or not the i-th pixel exists inside the bone of interest. If the i-th pixel exists, the process proceeds to step S1515; otherwise, it returns to the higher-level processing.

[0289] (Step S1515) The bone-transparent section 61 obtains the pixel value of the i-th pixel inside the bone.

[0290] (Step S1516) The bone-transmitting unit 61 obtains the correction amount to be used for the i-th pixel. For example, if the i-th pixel is close to the upper line, the bone-transmitting unit 61 obtains an upward correction amount, and if the i-th pixel is close to the lower line, it obtains a downward correction amount. Alternatively, the bone-transmitting unit 61 may obtain the average value of the upward correction amount and the downward correction amount.

[0291] (Step S1517) The bone-transparent portion 61 obtains a corrected pixel value by "pixel value of the i-th pixel - correction amount obtained in step S1516".

[0292] (Step S1518) The bone-transmitting unit 61 uses the corrected pixel value obtained in step S1517 as the pixel value of the i-th pixel. For example, the bone-transmitting unit 61 overwrites the position of the i-th pixel with the corrected pixel value.

[0293] (Step S1519) The bone-transparent portion 61 increments counter i by 1. Return to step S1514.

[0294] Next, an example of the bone margin influence correction process in step S1407 will be explained using the flowchart in Figure 16.

[0295] (Step S1601) The pixel value correction means 611 assigns 1 to counter i.

[0296] (Step S1602) The pixel value correction means 611 determines whether or not the i-th pixel to be judged exists. If the i-th pixel to be judged exists, the process proceeds to step S1603; otherwise, it returns to the higher-level processing.

[0297] The pixels subject to judgment are those that are judged to determine whether or not the abnormal condition is met as a result of the bone transparency processing. The pixels subject to judgment may be all pixels that have undergone bone transparency processing, but it is preferable that they be within a threshold range from the bone contour line (upper line or lower line). The range within the threshold range from the bone contour line is, for example, a range in which each pixel constituting the bone contour line has the same X coordinate value, and the difference in Y coordinate values ​​is within a threshold (for example, "5").

[0298] (Step S1603) The pixel value correction means 611 obtains the corrected pixel value of the i-th pixel to be judged.

[0299] (Step S1604) The pixel value correction means 611 obtains the corrected pixel values ​​of two or more neighboring pixels that have the same X coordinate value as the i-th pixel (x, y) to be judged. The two or more neighboring pixels are pixels inside the bone, for example, pixels (x, y+1), (x, y+2), and (x, y+3).

[0300] (Step S1605) The pixel value correction means 611 obtains a representative value (for example, an average value or a median value) of two or more corrected pixel values obtained in step S1604. Note that the pixel value correction means 611 may obtain a standard deviation of two or more corrected pixel values obtained in step S1604.

[0301] (Step S1606) The pixel value correction means 611 uses the corrected pixel value of the i-th pixel to be determined and the representative value of the corrected pixel values obtained in step S1605 to determine whether the corrected pixel value of the i-th pixel to be determined meets the abnormal condition. If it meets the abnormal condition, it proceeds to step S1607; if it does not meet the condition, it proceeds to step S1609.

[0302] (Step S1607) The pixel value correction means 611 obtains a pixel value to be adopted as the i-th pixel to be determined. The pixel to be adopted is, for example, a pixel having the same Y coordinate value as the pixel, and is a representative value (for example, an average value or a median value) of pixel values of pixels within a threshold value (for example, 2 or 3) immediately outside the boundary line of the bone (outside the bone).

[0303] (Step S1608) The pixel value correction means 611 sets the pixel value obtained in step S1607 as the pixel value of the i-th pixel to be determined. Note that the pixel value correction means 611 overwrites the pixel value obtained in step S1607 at the position of the i-th pixel to be determined.

[0304] (Step S1609) The pixel value correction means 611 increments the counter i by 1. It returns to step S1602.

[0305] Note that it may be used in the flowchart of FIG. ~.

[0306] Hereinafter, a specific operation example of the image processing apparatus B in the present embodiment will be described.

[0307] The clavicle line acquisition unit 32 of the image processing device B detects the upper clavicle line and the lower clavicle line from the chest transmission image in Figure 10(a). The rib line acquisition unit 33 also detects the upper rib line and the lower rib line from the chest transmission image in Figure 10(a). The clavicle line acquisition unit 32 temporarily stores information identifying the detected upper clavicle line and the lower clavicle line in the clavicle line storage unit 51. The rib line acquisition unit 33 also temporarily stores information identifying the detected upper rib line and the lower rib line in the rib line storage unit 52.

[0308] Next, the bone-transmitting unit 61 performs the bone-transmitting process described above on the clavicle region between the superior and inferior clavicle lines. The bone-transmitting unit 61 also performs the bone-transmitting process described above on the regions of two or more ribs between the superior and inferior costal lines.

[0309] Next, the bone transparency image output unit 71 outputs a bone transparency image in which the bone portion has been made transparent after the bone transparency processing has been performed.

[0310] The results of the above processing will be explained using Figure 17. In other words, as a result of the above processing, the image in Figure 17(a), a translucent chest image in which the ribs are visible, is output as a translucent bone image (Figure 17(b)) in which the rib region is made transparent. In the above processing, information that is thought to originate from the bone structure is subtracted from the translucent chest image, so the information that appears to overlap with the ribs in Figure 17(b) is preserved. In Figure 17(b), it can be seen that the vascular structure (indicated by the arrow) is preserved.

[0311] As described above, according to this embodiment, a bone radiolucent image can be obtained by removing the bone from a chest radiolucent image. Furthermore, according to this embodiment, the ability to output a bone radiolucent image has the effect of preventing oversight of lung lesions, including early-stage lung cancer. In addition, according to this embodiment, it is thought that the risk of medical malpractice lawsuits may be reduced, making it useful from the standpoint of medical safety. Moreover, because it is possible to output a bone radiolucent image, it may also be applicable to screening for COVID-19, which has had an extremely severe impact recently.

[0312] In this specific example, the lesion detection unit 62 may perform lesion detection processing on the bone transparency image. The processing unit 6 may then create an image that clearly indicates the lesion on the bone transparency image, and the output unit 7 may output this image.

[0313] Furthermore, in this embodiment, one or more bone radiolucent images output by the image processing device B may be used as training data, and a learning device (not shown) may be used to perform machine learning training to create a learner. The training data may consist of, for example, one or more bone radiolucent images with lesions such as lung cancer (positive examples) and one or more bone radiolucent images without lesions such as lung cancer (negative examples). The prediction device (not shown) can use the bone radiolucent images obtained from the chest radiolucent images and the learner obtained by the learning device to perform machine learning prediction processing to output whether or not there are lesions such as lung cancer.

[0314] Furthermore, in this embodiment, two or more sets of bone radiolucent images output by the image processing device B and information identifying the lesion area (lesion area information) annotated for each bone radiolucent image may be used as training data, and a learning device (not shown) may be used to perform machine learning training to create a learner. In addition, a prediction device (not shown) can output the lesion area, such as lung cancer, by machine learning prediction processing using bone radiolucent images obtained from chest radiolucent images and the learner obtained by the learning device.

[0315] The algorithms used in the machine learning training and prediction processes described above are not limited to deep learning, random forests, decision trees, SVMs, etc. Furthermore, various machine learning functions and existing libraries can be used for machine learning, such as the TensorFlow library, the R language's random forest module, and TinySVM. The learner can also be referred to as a learning model, classifier, or classification model.

[0316] Furthermore, the processing in this embodiment may be implemented in software. This software may be distributed via software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments described herein. The software that implements the information processing device in this embodiment is the following program. In other words, this program is a program that causes a computer that can access an image storage unit where a chest transparency image is stored and a costal line storage unit where information identifying pixels constituting the lower costal line and information identifying pixels constituting the upper costal line is stored to function as a bone transparency unit that obtains a correction amount relating to the difference between an inner representative value, which is a representative value of the pixel value of the pixels inside the ribs of the lower costal line or the upper costal line, and an outer representative value, which is a representative value of the pixel value of the pixels outside the lower costal line or the upper costal line, corrects the pixel value of the rib pixels, which are pixels between the lower costal line and the upper costal line, using the correction amount, obtains a corrected pixel value, sets the corrected pixel value as the pixel value of the rib pixels, obtains a bone transparency unit that obtains a bone transparency image with the ribs passing through the chest transparency image, and a bone transparency image output unit that outputs the bone transparency image obtained by the bone transparency unit.

[0317] Figure 18 also shows the appearance of a computer that executes the program described herein to realize the bone structure contour extraction device A or image processing device B, etc., of the various embodiments described above. The embodiments described above can be realized with computer hardware and computer programs executed thereon. Figure 18 is an overview of this computer system 300, and Figure 19 is a block diagram of the system 300.

[0318] In Figure 18, the computer system 300 includes a computer 301 with a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0319] In Figure 19, the computer 301 includes, in addition to the CD-ROM drive 3012, an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012, a ROM 3015 for storing programs such as boot-up programs, a RAM 3016 connected to the MPU 3013 for temporarily storing application program instructions and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card for providing connectivity to a LAN.

[0320] The program that causes the computer system 300 to execute the functions of the bone structure contour extraction device A, etc., as described above, may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 during execution. The program may also be loaded directly from CD-ROM 3101 or the network.

[0321] The program does not necessarily have to include an operating system (OS) or third-party program that causes the computer 301 to execute functions such as the bone structure contour extraction device A of the above-described embodiment. The program only needs to include the instruction portion that calls the appropriate function (module) in a controlled manner and obtains the desired result. How the computer system 300 operates is well known, so a detailed explanation is omitted.

[0322] In the above program, steps such as sending information and receiving information do not include hardware-based processing, such as processing performed by a modem or interface card in the transmission step (processing that can only be performed by hardware).

[0323] Furthermore, the computer running the above program may be a single computer or multiple computers. In other words, it may perform centralized processing or distributed processing.

[0324] Furthermore, it goes without saying that in each of the above embodiments, two or more communication means present in a single device may be physically implemented in a single medium.

[0325] Furthermore, in each of the above embodiments, each process may be implemented by centralized processing by a single device, or by distributed processing by multiple devices.

[0326] It goes without saying that the present invention is not limited to the embodiments described above, and various modifications are possible, all of which are also included within the scope of the present invention. [Industrial applicability]

[0327] As described above, the bone structure contour extraction device according to the present invention has the effect of being able to appropriately detect bone from chest transmission images and is useful as a bone structure contour extraction device, etc. [Explanation of Symbols]

[0328] A Bone Structure Contour Extraction Device B Image Processing Device 1, 5 Storage section 2. Reception Department 3, 6 Processing Units 4, 7 Output section 11 Image storage section 31 Detection area in the lung field 32 Clavicle line acquisition part 33 Rib line acquisition part 41. Output section of the clavicle line 42. Rib line output section 51 Clavicle storage area 52. Rib cage storage section 61 Bone penetration area 62 Lesion detection unit 71 Bone Transparency Image Output Unit 72 Lesion output section 321 Candidate line detection means 322 Partial Line Detection Means 323 Clavicle extension means 324 Second clavicle line detection means 331 Rib line acquisition means 332 Correction means 611 Pixel value correction means

Claims

1. An image storage unit where chest-transparency images are stored, A clavicle line acquisition unit that uses the aforementioned transmissible chest image to acquire the inferior clavicle line, which is the lower boundary of the clavicle, and the superior clavicle line, which is the upper boundary of the clavicle, The system includes a clavicle line output unit that outputs the lower clavicle line and the upper clavicle line, The clavicle line acquisition section is, Candidate line detection means for analyzing the aforementioned chest radiographic image and detecting one or more candidate lines that are candidates for the boundary line of the clavicle, A partial line detection means that detects the pixel with the highest pixel value among the one or more candidate lines mentioned above, which matches the clavicle inclination condition and the clavicle line region condition in the chest transmission image, and detects the candidate line closest to that pixel as a partial line constituting the lower clavicle line or the upper clavicle line. A clavicle extension means for detecting the lower clavicle line or the upper clavicle line obtained by extending the partial line from the endpoint of the partial line, A bone structure contour extraction device comprising: a second clavicle line detection means for detecting a boundary point on the opposite side that satisfies the clavicle width condition for each point constituting the lower clavicle line or the upper clavicle line, and for detecting the upper clavicle line or the lower clavicle line that includes the boundary point.

2. The clavicle extension means is From among one or more candidate points extending the partial straight line, the bone structure contour extraction device according to claim 1 calculates an upper difference, which is the difference between the pixel value of a pixel above the candidate point and the pixel value of the candidate point, and a lower difference, which is the difference between the pixel value of the candidate point and the pixel value of a pixel below the candidate point, selects a candidate point where the larger of the upper difference and the lower difference is greater than or equal to a threshold, and where the difference between an upper representative value, which is a representative value of the pixel values ​​of two or more pixels above the candidate point, and a lower representative value, which is a representative value of the pixel values ​​of two or more pixels below the candidate point, is greater than or equal to a threshold, adopts the candidate point as a point constituting the lower clavicle line or the upper clavicle line, and detects the lower clavicle line or the upper clavicle line.

3. The clavicle extension means is The bone structure contour extraction device according to claim 2, wherein one or more candidate points are selected from among the points obtained by advancing one step in the X direction from the endpoint of the partial straight line, and the Y direction changes within a threshold relative to the endpoint, and each of the one or more candidate points is adopted as the point constituting the lower clavicle line or the upper clavicle line.

4. The clavicle extension means is A bone structure contour extraction device according to claim 2 or 3, wherein a reference candidate point is selected considering the slope of the aforementioned partial straight line, the difference in the Y coordinate value between each of the one or more candidate points and the reference candidate point is calculated, a score is obtained for each of the one or more candidate points using the difference, and the candidate point with the best score is adopted as the point constituting the lower clavicle line or the upper clavicle line.

5. The system further comprises a lung field detection unit that analyzes the aforementioned chest radiographic image and detects the lung field, The bone structure contour extraction device according to any one of claims 1 to 4, wherein the clavicle line acquisition unit acquires the inferior clavicle line and the superior clavicle line from the lung field.

6. A bone structure contour extraction method realized by an image storage unit that stores a translucent chest image, a clavicle line acquisition unit, and a clavicle line output unit, The clavicle line acquisition unit performs a clavicle line acquisition step, which uses the chest transmission image to acquire the lower clavicle line, which is the lower boundary of the clavicle, and the upper clavicle line, which is the upper boundary of the clavicle. The clavicle line output unit comprises a clavicle line output step that outputs the front lower clavicle line and the upper clavicle line, In the step of obtaining the clavicle line, A candidate line detection substep involves analyzing the aforementioned transmissible chest image and detecting one or more candidate lines that are candidates for the boundary line of the clavicle, A partial line detection substep is performed in which, among the one or more candidate lines mentioned above, the pixel with the highest pixel value is detected among the pixels in the range that matches the clavicle inclination condition and the clavicle line region condition in the chest transmission image, and the candidate line closest to that pixel is detected as a partial line constituting the lower clavicle line or the upper clavicle line. A clavicle extension substep for detecting the lower clavicle line or the upper clavicle line obtained by extending the partial line from the endpoint of the partial line, A bone structure contour extraction method comprising: a second clavicle line detection substep of detecting a boundary point on the opposite side that satisfies the clavicle width condition for each point constituting the lower clavicle line or the upper clavicle line, and detecting the upper clavicle line or the lower clavicle line that includes the boundary point.

7. A computer that can access the image storage unit where chest radiographic images are stored, A clavicle line acquisition unit that uses the aforementioned transmissible chest image to acquire the inferior clavicle line, which is the lower boundary of the clavicle, and the superior clavicle line, which is the upper boundary of the clavicle, It functions as a clavicle line output unit that outputs the lower clavicle line and the upper clavicle line. The clavicle line acquisition section is, Candidate line detection means for analyzing the aforementioned chest radiographic image and detecting one or more candidate lines that are candidates for the boundary line of the clavicle, A partial line detection means that detects the pixel with the highest pixel value among the one or more candidate lines mentioned above, which matches the clavicle inclination condition and the clavicle line region condition in the chest transmission image, and detects the candidate line closest to that pixel as a partial line constituting the lower clavicle line or the upper clavicle line. A clavicle extension means for detecting the lower clavicle line or the upper clavicle line obtained by extending the partial line from the endpoint of the partial line, A program for causing the computer to function, comprising: a second clavicle line detection means for detecting a boundary point on the opposite side that satisfies the clavicle width condition for each point constituting the lower clavicle line or the upper clavicle line, and for detecting the upper clavicle line or the lower clavicle line that includes the boundary point.

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