Ultrasonic diagnostic apparatus and ultrasonic diagnostic program

Abstract

To provide an ultrasonic diagnostic apparatus and an ultrasonic diagnostic program that facilitate evaluation of a tissue of a subject.SOLUTION: An irregularity analysis unit 22 executes, for each of a plurality of B-mode images acquired at different positions in a subject 14, feature point extraction processing for extracting a plurality of feature points, and evaluation processing for generating information indicating irregularity of a tissue within the subject 14 on the basis of the extracted feature points of each of the plurality of B-mode images. The evaluation processing includes: processing for executing distance evaluation processing for calculating, for each feature point in each of two B-mode images, a feature point distance between a feature point of one of the two B-mode images and a feature point of the other image for each image pair consisting of a reference B-mode image in the plurality of B-mode images and each of other B-mode images; and processing for generating information indicating irregularity on the basis of the feature point distance calculated for each feature point in each image pair.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an ultrasonic diagnostic apparatus and an ultrasonic diagnostic program, and more particularly to evaluation of tissue within a subject. [Background technology]

[0002] Ultrasound diagnostic devices are used to determine whether a tumor is benign or malignant. Generally, the determination of whether a tumor is benign or malignant is performed manually by a doctor who refers to ultrasound images such as B-mode images acquired by an ultrasound diagnostic device. Cited Document 1 describes the diagnosis of hemangioma using multiple ultrasound images acquired in time series. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-54815 Summary of the Invention [Problem to be solved by the invention]

[0004] Malignant tumors are characterized by a higher degree of irregularity in their internal structure compared to benign tumors. Focusing on this characteristic, it has been considered to use ultrasound diagnostic equipment to determine whether a tumor is benign or malignant. However, this requires a great deal of experience and effort on the part of the physician. Therefore, there is a need for technology that can easily determine whether a tumor is benign or malignant using ultrasound diagnostic equipment.

[0005] An object of the present invention is to provide an ultrasonic diagnostic apparatus and an ultrasonic diagnostic program that facilitate evaluation of tissues of a subject. That is the thing. [Means for solving the problem]

[0006] The ultrasound diagnostic device according to the present invention is characterized by comprising an information processing unit that executes a feature point extraction process that extracts a plurality of feature points for each of a plurality of ultrasound images acquired at different positions on a subject, and an evaluation process that generates information indicative of tissue irregularities within the subject based on the feature points extracted for each of the plurality of ultrasound images.

[0007] In one embodiment, the evaluation process includes a process of performing a distance evaluation process for each image pair consisting of a reference ultrasound image, which is one of the plurality of ultrasound images, and each of the other plurality of ultrasound images, to determine a feature point distance between one feature point of the two ultrasound images and a feature point corresponding to the one feature point of the other ultrasound image, and a process of generating information indicating the irregularity based on the feature point distance determined for each feature point in each of the image pairs.

[0008] In one embodiment, the distance evaluation process includes a process of determining the deviation of one of the images in the image pair from the reference ultrasound image, correcting the position of the feature point of the other image based on the determined deviation, and then determining the feature point distance.

[0009] In one embodiment, the information processing unit determines a color for each feature point in the reference ultrasound image based on the feature point distance calculated for each of the image pairs, and generates an image in which local regions corresponding to each feature point in the reference ultrasound image are colored.

[0010] In one embodiment, the evaluation process includes a process of performing a feature point contour evaluation process to determine a feature point contour defined by a plurality of feature points for each of the plurality of ultrasound images, and to determine difference information between one feature point contour and the other feature point contour for each image pair between a reference ultrasound image that is one of the plurality of ultrasound images and each of the other plurality of ultrasound images, and a process of generating information indicating the irregularity based on the difference information determined for each of the image pairs.

[0011] In one embodiment, the feature point contour evaluation process includes a process of determining the deviation of one of the image pairs relative to the reference ultrasound image, and a process of correcting the position of the feature point contour of the other image based on the determined deviation, and then determining the difference information.

[0012] In one embodiment, the feature points are points where a feature amount indicating a geometric feature appearing in the ultrasound image is a local maximum or a point where the feature amount exceeds a predetermined threshold.

[0013] Furthermore, the ultrasound diagnostic program according to the present invention is characterized in that it causes a processor to execute a feature point extraction process that extracts a plurality of feature points for each of a plurality of ultrasound images acquired at different positions in the subject, and an evaluation process that generates information indicating tissue irregularities within the subject based on the feature points extracted for each of the plurality of ultrasound images.

[0014] In one embodiment, the evaluation process includes a process of performing a distance evaluation process for each image pair consisting of a reference ultrasound image, which is one of the plurality of ultrasound images, and each of the other plurality of ultrasound images, to determine a feature point distance between one feature point of the two ultrasound images and a feature point corresponding to the one feature point of the other ultrasound image, and a process of generating information indicating the irregularity based on the feature point distance determined for each feature point in each of the image pairs.

[0015] In one embodiment, the evaluation process includes a process of performing a feature point contour evaluation process to determine a feature point contour defined by a plurality of feature points for each of the plurality of ultrasound images, and to determine difference information between one feature point contour and the other feature point contour for each image pair between a reference ultrasound image that is one of the plurality of ultrasound images and each of the other plurality of ultrasound images, and a process of generating information indicating the irregularity based on the difference information determined for each of the image pairs. [Effects of the Invention]

[0016] According to the present invention, it is possible to easily evaluate the tissue of a subject. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a diagram showing the configuration of an ultrasound diagnostic apparatus according to an embodiment of the present invention. [Figure 2] FIG. 10 is a diagram showing analysis target data. [Figure 3] FIG. 10 is a diagram illustrating a process for obtaining a feature point evaluation value. [Figure 4] 10 is a flowchart of a process for calculating a feature point evaluation value. [Figure 5] FIG. 10 is a diagram showing a color map image. [Figure 6] FIG. 10 is a diagram showing a process in which analysis image data is generated sequentially over time. [Figure 7] FIG. 10 is a diagram showing an example of processing for obtaining feature point contours for each B-mode image data. [Figure 8] 10 is a flowchart of a process for generating analysis image data based on feature point contours obtained for each of n frames of B-mode image data. [Figure 9] FIG. 10 is a diagram showing that the irregularity index determination process is executed for the reference B-mode image data and the B-mode image data of the second to n-th frames, respectively. [Figure 10] FIG. 10 is a diagram showing the feature point outlines obtained for the reference B-mode image data and the feature point outlines obtained for B-mode image data of another frame. [Figure 11] FIG. 10 is a diagram showing an example of processing for calculating an evaluation area of the feature point contour for each B-mode image data. [Figure 12] FIG. 10 is a diagram illustrating a correction process for feature points. DETAILED DESCRIPTION OF THE INVENTION

[0018] An embodiment of the present invention will be described with reference to the drawings. Identical components shown in multiple drawings are assigned the same reference numerals, and their description will be omitted. FIG. 1 shows the configuration of an ultrasound diagnostic apparatus 100 according to an embodiment of the present invention. The ultrasound diagnostic apparatus 100 includes a transmitter 10, an ultrasound probe 12, a receiver 16, an information processing unit 34, a display 28, a controller 30, and an operation unit 32. The operation unit 32 may include buttons, levers, a keyboard, a mouse, etc. The operation unit 32 may be a touch panel provided on the display 28. The controller 30 performs overall control of the ultrasound diagnostic apparatus 100 in response to user operations on the operation unit 32.

[0019] The ultrasonic probe 12 includes a plurality of transducer elements. The transmitter 10 outputs a transmission signal to each transducer element. Each transducer element converts the transmission signal into ultrasound waves and transmits them to the subject 14. The transmitter 10 adjusts the delay time of the transmission signal output to each transducer element so that the ultrasound waves emitted from each transducer element reinforce each other in a specific direction. This forms a transmission beam of ultrasound waves in that specific direction. The transmitter 10 changes the delay time of the transmission signal output to each transducer element and scans the transmission beam across the subject 14.

[0020] Each of the multiple transducer elements receives ultrasound waves reflected by the subject 14, converts them into electrical signals, and outputs them to the receiver 16. The receiver 16 generates a received signal by phasing and adding the electrical signals output from each transducer element so that electrical signals based on ultrasound waves received from the direction of the transmitted beam reinforce each other, and outputs this received signal to the information processor 34. A received beam is formed in the ultrasound probe 12 by phasing and adding. Here, the received beam refers to a directivity pattern that indicates the direction from which the ultrasound waves that reinforce the received signals arrive. The received signal corresponding to the received beam is output from the receiver 16 to the information processor 34 as a signal for generating B-mode image data. In the following description, the transmitted beam and the received beam are collectively referred to as a transmitted / received beam.

[0021] The information processing unit 34 includes a B-mode image generating unit 18, an image memory 20, an irregularity analyzing unit 22, an analysis image generating unit 24, and a display processing unit 26. The information processing unit 34 may include a processor that realizes the functions of each component by executing a program. In this case, the processor configures each component (the B-mode image generating unit 18, the image memory 20, the irregularity analyzing unit 22, the analysis image generating unit 24, and the display processing unit 26) by executing the program.

[0022] The received signals output from the receiving unit 16 are input to the B-mode image generating unit 18. The B-mode image generating unit 18 generates B-mode image data based on the received signals obtained in each scanning direction of the transmitted and received beams.

[0023] The transmitter 10, ultrasonic probe 12, and receiver 16 repeatedly scan the subject 14 with transmitted and received beams. The B-mode image generator 18 sequentially generates B-mode image data over time at a predetermined frame rate.

[0024] The ultrasound diagnostic apparatus 100 may operate in either a B-mode or an irregularity analysis mode, which will be described below.

[0025] In the B-mode operation, the display processing unit 26 generates video signals representing B-mode images sequentially over time based on the B-mode image data sequentially generated over time, and outputs the video signals to the display 28. In this case, the display 28 displays images based on the B-mode images sequentially generated over time based on the video signals, i.e., real-time B-mode images.

[0026] In operation in the irregularity analysis mode, the ultrasound diagnostic apparatus 100 stores B-mode image data sequentially acquired over time and performs an irregularity analysis process to analyze tissue irregularities in the subject 14. In the irregularity analysis process, the ultrasound probe 12 is transported along the surface of the subject 14. The ultrasound probe 12 may be transported by a user manually. Alternatively, a mechanism configured for transportation may be used. The direction of transport of the ultrasound probe 12 is a direction intersecting the plane on which the transmitted and received beams are scanned, i.e., the observation plane on which the B-mode image is acquired. The direction of transport of the ultrasound probe 12 may be perpendicular to the observation plane. A series of B-mode image data (hereinafter, sometimes referred to as analysis target data) sequentially generated over time by the B-mode image generation unit 18 is stored in the image memory 20. The analysis target data stored in the image memory 20 is the subject of the irregularity analysis process.

[0027] An area for storing n frames of B-mode image data as data to be analyzed may be secured in the image memory 20. In this case, when the number of B-mode images stored in the image memory 20 reaches n frames and a new B-mode image is generated, the oldest B-mode image data may be deleted from the image memory 20, and the new B-mode image may then be stored in the image memory 20.

[0028] The irregularity analysis unit 22 performs an irregularity analysis process on the analysis target data stored in the image memory 20 and generates irregularity information. The irregularity information is information indicating the irregularity of the tissue from which the analysis target data was obtained. The analysis image generation unit 24 generates analysis image data indicating the irregularity of the tissue and outputs it to the display processing unit 26. The display processing unit 26 generates a video signal indicating an analysis image based on the analysis image data and outputs the video signal to the display 28. The display 28 displays the analysis image based on the video signal.

[0029] The irregularity analysis process will be described in detail. FIG. 2 conceptually illustrates the analysis target data. The analysis target data is composed of n frames of B-mode image data representing n B-mode images F1 to Fn. The analysis target data is generated when the ultrasound probe 12 is transported in a direction perpendicular to the observation plane. In the example shown in FIG. 2, the observation plane of each B-mode image is parallel to the xy plane, and the analysis target data represents n B-mode images aligned in the z-axis direction perpendicular to the xy plane. In the following description, the B-mode image data representing the B-mode images F1 to Fn may be referred to as the B-mode image data of the first frame to the nth frame, respectively.

[0030] The irregularity analysis unit 22 sets a region of interest in the B-mode image for each of n frames of B-mode image data constituting the analysis target data, and calculates the distribution of feature quantities in the region of interest. Here, feature quantities refer to values that indicate geometric features that appear in the B-mode image. In this embodiment, feature quantities indicate the degree to which corners of linear images appear in the image. Feature quantities are larger at and around corners of linear images than in surrounding areas. Methods for calculating feature quantities include Harris's corner detection method and the minimum eigenvalue method.

[0031] The irregularity analysis unit 22 extracts feature points from each of the n frames of B-mode image data that make up the analysis target data. Here, a feature point refers to a point where a feature amount is maximized or a point where a feature amount exceeds a predetermined threshold. Extracting a feature point means determining the position coordinates where the feature point exists and acquiring the feature amount at that feature point.

[0032] The irregularity analyzer 22 selects one frame of B-mode image data from the n frames of B-mode image data constituting the analysis target data as reference B-mode image data. The reference B-mode image data may be, for example, the first acquired B-mode image data (the B-mode image data of the first frame) that has the smallest z-axis coordinate value. In the following, an embodiment will be described in which the B-mode image data of the first frame is used as the reference B-mode image data, but the reference B-mode image data may be any B-mode image data selected from the second to n-th frames.

[0033] The irregularity analysis unit 22 calculates the feature point distance between a plurality of feature points indicated by the reference B-mode image data and corresponding feature points in the B-mode image data of each of the other frames. Here, the feature point corresponding to the feature point indicated by the reference B-mode image data refers to the feature point on the other B-mode image that is closest to the feature point on the reference B-mode image when the reference B-mode image and the other B-mode image are superimposed. Furthermore, the feature point distance refers to the distance between the feature point on the reference B-mode image and the corresponding feature point on the other B-mode image when the reference B-mode image and the other B-mode image are superimposed. The irregularity analysis unit 22 further calculates, as a feature point evaluation value, the average of the feature point distances calculated between the plurality of feature points indicated by the reference B-mode image data and the B-mode image data of each of the other frames.

[0034] FIG. 3 conceptually illustrates the process of determining the feature point evaluation value. Five feature points C are shown on the reference B-mode image 40-1. Arrows are drawn from each feature point C on the reference B-mode image 40-1 to the corresponding feature point on the B-mode image 40-2 of the second frame. For each feature point C indicated by the reference B-mode image data, the feature point distance is determined between that feature point C and the feature point on the B-mode image 40-2 of the second frame that corresponds to that feature point C. The feature point distance for each feature point C on the reference B-mode image 40-1 is similarly determined for each of the B-mode image 40-3 of the third frame to the B-mode image 40-n of the nth frame. The average of the feature point distances determined for each of the second to nth frames is then determined as the feature point evaluation value for feature point C.

[0035] There are k feature points on the reference B-mode image, and the number identifying each feature point is j (j = 1 to k). Furthermore, the number i identifying the reference B-mode image is i = 1, and the numbers identifying the other B-mode images are i = 2 to n. The feature point distance between the j-th feature point on the reference B-mode image and the j-th feature point on the i-th B-mode image is d(j, i), and the feature point evaluation value Dj for feature points j = 1 to k is expressed by (Equation 1). The symbol Σ in (Equation 1) means to find the sum for i = 2 to n.

[0036] (Equation 1) Dj = (1 / (n-1)) Σd(j,i)

[0037] The irregularity analysis unit 22 may obtain the average value of the feature point evaluation values Dj for j=1 to k as the texture evaluation value D. The texture evaluation value D indicates the texture irregularity, and the greater the irregularity, the greater the value.

[0038] 4 shows a flowchart of the process for calculating the feature point evaluation value. In this flowchart, the B-mode image represented by the B-mode image data of the i-th frame is simply referred to as the i-th B-mode image. The irregularity analysis unit 22 selects the j-th feature point Pj (j=1 to k) from among the k feature points on the reference B-mode image (S101). Note that the initial value of j is 1.

[0039] The irregularity analysis unit 22 searches for the feature point closest to the feature point Pj from all feature points on the i-th (i=2 to n) B-mode image, calculates the feature point distance d(i, j) (S102), and stores the feature point distance d(i, j) (S103), where the initial value of i is 2.

[0040] The irregularity analysis unit 22 determines whether i is n (S104), and if i is not n, increments i by 1 (S108) and returns to the processing of step S102. If i is n, the irregularity analysis unit 22 calculates the average value of the feature point distances d(i,j) for i = 2 to n as the feature point evaluation value Dj (S105).

[0041] The irregularity analysis unit 22 determines whether j is k (S106), and if j is not k, increments j by 1 (S109) and returns to the processing of step S101. If j is k, the irregularity analysis unit 22 calculates the average value of the feature point evaluation values Dj for j = 1 to k as the texture evaluation value D (S107).

[0042] The irregularity analysis unit 22 outputs the irregularity information and the reference B-mode image data to the analysis image generation unit 24. Here, the irregularity information may include feature point evaluation values Dj (j = 1 to k) calculated for k feature points on the reference B-mode image, position information of the k feature points, feature amounts of the k feature points, and a tissue evaluation value D. The analysis image generation unit 24 generates analysis image data indicating the analysis image. The analysis image may be an image indicating the B-mode image indicated by the reference B-mode image data, the position of each feature point, the feature amount of each feature point on the reference B-mode image, the feature point evaluation value of each feature point on the reference B-mode image, the tissue evaluation value D, etc.

[0043] The analysis image generating unit 24 outputs the analysis image data to the display processing unit 26. The display processing unit 26 generates a video signal representing the analysis image based on the analysis image data, and outputs the video signal to the display 28. The display 28 displays the analysis image based on the video signal. The user may determine whether the tissue of the subject 14 is irregular by referring to the analysis image displayed on the display 28. Furthermore, based on this determination, the user may determine whether the subject 14 has a tumor or the like. Furthermore, if it is determined that a tumor is present, the user may determine whether the tumor is benign or malignant.

[0044] In this way, the irregularity analysis unit 22 performs a feature point extraction process to extract multiple feature points for each of multiple B-mode images (ultrasound images) acquired at different positions in the subject 14, and an evaluation process to generate information indicating the irregularity of the tissue in the subject 14 based on the feature points extracted for each of the multiple B-mode images.

[0045] This evaluation process includes a process of performing a distance evaluation process for each image pair consisting of a reference B-mode image, which is one of the plurality of B-mode images, and each of the other plurality of B-mode images, to determine the feature point distance between one feature point of the two B-mode images and the corresponding feature point of the other B-mode image, and a process of generating information indicating irregularity based on the feature point distance determined for each feature point in each image pair.

[0046] An ultrasound diagnostic program for executing the above-described feature point extraction processing and evaluation processing may be loaded into a processor constituting the irregularity analysis unit 22 in the information processing unit 34. That is, the ultrasound diagnostic program causes the processor to execute the above-described feature point extraction processing and evaluation processing.

[0047] FIG. 5 shows a color map image 42 as an example of an analysis image. In this example, the position and feature point evaluation value of each feature point are shown together with tissue T by a circle centered on the position of each feature point. The radius of the circle indicates the feature point evaluation value. The color inside the circle may also indicate the feature point evaluation value. For example, the larger the feature point evaluation value, the longer the wavelength (redder the color) is displayed inside the circle, and the smaller the feature point evaluation value, the shorter the wavelength (bluer the color) is displayed inside the circle. Note that instead of displaying the radius of the circle based on the feature point evaluation value, a display may be performed in which the radius of the circle is determined by the feature amount in the reference B-mode image.

[0048] In this way, the analysis image generation unit 24 determines the color for each feature point in the reference B-mode image (reference ultrasound image) based on the feature point distance calculated for each image pair, and generates an image in which the inside of the circle (local area) corresponding to each feature point in the reference B-mode image is colored.

[0049] The ultrasound diagnostic program loaded into the processor constituting the analysis image generation unit 24 causes the processor to execute a process of determining a color for each feature point in the reference B-mode image (reference ultrasound image) based on the feature point distance calculated for each image pair, and generating an image in which a color is applied to the inside of a circle (local region) corresponding to each feature point in the reference B-mode image.

[0050] 6 conceptually illustrates the process of generating analysis image data sequentially over time. When the B-mode image data of the nth frame is stored in the image memory 20, the irregularity analyzer 22 performs irregularity analysis processing on the B-mode image data from the 1st frame to the nth frame stored going back to the n-1th frame, thereby generating analysis image data 1.

[0051] When the B-mode image data of the (n+1)th frame is stored in the image memory 20, the irregularity analysis unit 22 generates analysis image data 2 based on the B-mode image data from the 2nd frame to the (n+1)th frame stored going back to the n-1th frame....When the B-mode image data of the (n+L)th frame is stored in the image memory 20, the irregularity analysis unit 22 generates analysis image data L based on the B-mode image data from the 1+Lth frame to the (n+L)th frame stored going back to the n-1th frame.

[0052] In this way, each time new B-mode image data is stored in the image memory 20, the irregularity analysis unit 22 sequentially generates new analysis image data based on a series of n frames of B-mode image data (analysis target data) stored going back to the original B-mode image data up to n-1 frames.

[0053] Next, we will explain the irregularity analysis process based on the feature point contours. The feature point contours are shapes formed by connecting the closest feature points on a B-mode image with lines.

[0054] The irregularity analysis unit 22 executes a process of extracting feature points for each of n frames of B-mode image data constituting the analysis target data. The irregularity analysis unit 22 obtains feature point contour data indicating the contours of the feature points for each B-mode image data.

[0055] FIG. 7 conceptually illustrates an example of a process for determining the feature point contours for each B-mode image data. The irregularity analysis unit 22 first searches for the first feature point C1 with the largest feature amount. The irregularity analysis unit 22 then searches for the second feature point C2 closest to the first feature point C1 and determines connection data representing lines connecting the first feature point C1 and the second feature point C2. The irregularity analysis unit 22 then performs similar processing until it has searched for a total of k feature points. The irregularity analysis unit 22 sequentially connects the first feature point C1 to the kth feature point Ck and determines connection data representing each line connecting the kth feature point Ck and the first feature point C1. The connection data sequentially connects the first feature point C1 to the kth feature point Ck and each line connecting the kth feature point Ck and the first feature point C1 is feature point contour data that indicates the feature point contours.

[0056] 8 shows a flowchart of a process for generating analysis image data based on the feature point outlines determined for each of n frames of B-mode image data. The irregularity analysis unit 22 assigns an evaluation index of 1 to each predetermined evaluation position inside the feature point outline determined for the reference B-mode image data, and assigns an evaluation index of 0 to each evaluation position outside the feature point outline (S201).

[0057] In addition, for each of the other frames, the irregularity analysis unit 22 assigns an evaluation index of -1 to each evaluation position inside the feature point outline obtained for the B-mode image data, and assigns an evaluation index of 0 to each evaluation position outside the feature point outline (S202).

[0058] The irregularity analysis unit 22 adds, for each evaluation position, the evaluation index assigned to each evaluation position on the B-mode image for the other frame and the evaluation index assigned to each evaluation position on the reference B-mode image, to determine an irregularity index for each evaluation position. An evaluation position with a positive irregularity index is located inside the feature point outline on the reference B-mode image and outside the feature point outline on the B-mode image for the other frame. An evaluation position with a negative irregularity index is located outside the feature point outline on the reference B-mode image and inside the feature point outline on the B-mode image for the other frame. An evaluation position with an irregularity index of 0 is located inside the feature point outline on the reference B-mode image and inside the feature point outline on the B-mode image for the other frame. The irregularity analysis unit 22 performs this irregularity index determination process for each of the B-mode image data of the other n-1 frames that are not the reference B-mode image data (S203).

[0059] FIG. 9 conceptually shows that the irregularity index determination process is executed on the B-mode image data of the first frame, i.e., the reference B-mode image data, and the B-mode image data of the second to n-th frames.

[0060] 10 conceptually shows a feature point outline 50 calculated for the reference B-mode image data and a feature point outline 52 calculated for the B-mode image data of another frame. At evaluation positions where the irregularity index is positive, the feature point outline in the B-mode image of another frame is smaller than the feature point outline in the reference B-mode image. At evaluation positions where the irregularity index is negative, the feature point outline in the B-mode image of another frame is larger than the feature point outline in the reference B-mode image. At evaluation positions where the irregularity index is 0, the feature point outline in the B-mode image of another frame is neither enlarged nor reduced compared to the feature point outline in the reference B-mode image.

[0061] The irregularity index at each evaluation position can be said to be difference information indicating the difference between the feature point outline 50 obtained for the reference B-mode image data and the feature point outline 52 obtained for the B-mode image data of another frame.

[0062] Returning to FIG. 8, the irregularity analysis unit 22 adds up the irregularity indices calculated for each of the n-1 frames individually for each evaluation position, and calculates an irregularity evaluation value for each evaluation position (S204).

[0063] The irregularity analysis unit 22 outputs irregularity information including the irregularity evaluation value calculated for each evaluation position and the reference B-mode image data to the analysis image generation unit 24. The analysis image generation unit 24 generates analysis image data representing an analysis image (S205). The analysis image is an image in which each evaluation position on the B-mode image represented by the reference B-mode image data and the periphery of each evaluation position are colored in accordance with the irregularity evaluation value. Here, the periphery of an evaluation position refers to, for example, a region within a circle defined by a predetermined radius centered on the evaluation position. Alternatively, the periphery of an evaluation position may be a polygonal region including the evaluation position. The larger the absolute value of the irregularity evaluation value, the longer the wavelength of the color (redder), and the smaller the absolute value of the irregularity evaluation value, the shorter the wavelength of the color (bluer),

[0064] The irregularity analysis unit 22 may calculate a tissue evaluation value D, which indicates the irregularity of biological tissue, based on the analysis target data by the following process. The irregularity analysis unit 22 calculates a correlation value by performing a correlation calculation between the feature point outline calculated for the reference B-mode image data and the feature point outline calculated for the B-mode image data of other frames. The correlation value indicates the degree of similarity between the two feature point outlines. The larger the correlation value, the more similar the two feature point outlines are. The irregularity analysis unit 22 calculates, for example, the reciprocal of the average of the correlation values calculated for each of the second to n-th frames as the tissue evaluation value D. The tissue evaluation value D may be calculated based on the reciprocal of the average of the correlation values or on other relationships such that the tissue evaluation value D increases as the average correlation value decreases. The greater the difference between the feature point outline calculated for the reference B-mode image data and the feature point outline calculated for the B-mode image data of each of the other frames, the smaller the average of the correlation values and the larger the tissue evaluation value D.

[0065] The irregularity analysis unit 22 outputs irregularity information including the tissue evaluation value D to the analysis image generation unit 24. The analysis image generation unit 24 generates analysis image data indicating the tissue evaluation value D and outputs the data to the display processing unit 26. The display processing unit 26 generates a video signal indicating an analysis image based on the analysis image data and outputs the video signal to the display 28. The display 28 displays the analysis image based on the video signal.

[0066] The irregularity analysis unit 22 may obtain the tissue evaluation value D, which indicates the irregularity of the tissue, by another process described below. The irregularity analysis unit 22 obtains the feature point outline for each of n frames of B-mode image data constituting the analysis target data, and obtains the evaluation area for the feature point outline obtained for each B-mode image data.

[0067] FIG. 11 conceptually illustrates an example of processing for determining the evaluation area of the feature point contours for B-mode image data. The irregularity analysis unit 22 extracts feature points C from each piece of B-mode image data. The irregularity analysis unit 22 determines the coordinates of a representative point R in an area where the extracted feature points C exist. The x-coordinate value of the representative point R is, for example, the sum of the x-coordinate value (maximum x-coordinate value) of the feature points with the largest x-coordinate value and the x-coordinate value (minimum x-coordinate value) of the feature points with the smallest x-coordinate value, and the result is divided by 2. The y-coordinate value of the representative point R is, for example, the sum of the y-coordinate value (maximum y-coordinate value) of the feature points with the largest y-coordinate value and the y-coordinate value (minimum y-coordinate value) of the feature points with the smallest y-coordinate value, and the result is divided by 2. The irregularity analysis unit 22 searches for the feature point Cm that is farthest from the representative point R, and determines the area of a circle CM whose radius is the distance between the representative point R and the searched feature point Cm as the evaluation area.

[0068] The irregularity analysis unit 22 calculates the area difference value as the absolute value of the value obtained by subtracting the evaluation area calculated for the reference B-mode image data from the evaluation area calculated for the B-mode image data of the other frames. The area difference value indicates the degree to which the contours of the two feature points differ. The greater the area difference, the more different the contours of the two feature points are. The irregularity analysis unit 22 calculates, for example, the average value of the area difference values calculated for each of the second to n-th frames as the tissue evaluation value D. The greater the difference between the evaluation area calculated for the reference B-mode image data and the evaluation area calculated for the B-mode image data of the other frames, the greater the area difference value and the greater the tissue evaluation value D.

[0069] In this way, the evaluation process executed by the irregularity analysis unit 22 includes a first process of executing a feature point outer shape evaluation process that calculates a feature point outer shape defined by a plurality of feature points for each of a plurality of B-mode images (ultrasound images) and calculates difference information between one feature point outer shape and the other feature point outer shape for each image pair consisting of a reference B-mode image, which is one of the plurality of B-mode images, and each of the other plurality of B-mode images, and a second process of generating information indicating irregularity based on the difference information calculated for each image pair. Here, the difference information includes the above-mentioned irregularity index, correlation value, area difference value, etc.

[0070] The ultrasound diagnostic program loaded into the processor constituting the irregularity analysis unit 22 causes the processor to execute the first process and the second process described above.

[0071] In the above embodiments, it has been explained that there is no deviation in the position of tissues appearing in the B-mode images of each frame. In diagnosis in irregularity analysis mode, the position of tissues appearing in the B-mode images of other frames may be deviated from the B-mode image of the first frame due to camera shake or the like. Therefore, to compensate for the deviation in the position of tissues, correction processing may be performed on the positions of feature points in the second to n-th frames.

[0072] The irregularity analysis unit 22 performs correlation calculations while changing the position of the B-mode image of another frame relative to the reference B-mode image, determines the position of the B-mode image of another frame at which the correlation value is maximized, and determines the amount of positional deviation of the tissue appearing in the B-mode image of another frame relative to the tissue appearing in the reference B-mode image as a positional deviation vector.

[0073] That is, the irregularity analyzing unit 22 obtains a positional deviation vector for each of the second to n-th frames. The irregularity analyzing unit 22 subtracts the positional deviation vector obtained for each of the second to n-th frames from the position coordinates of the feature points obtained for each of the second to n-th frames. In this way, the irregularity analyzing unit 22 obtains new position coordinates of the feature points obtained for each of the second to n-th frames, and corrects the position coordinates of each feature point.

[0074] Figure 12(a) shows a reference B-mode image acquired in the xy plane. The reference B-mode image shows tissue T1, and an area X1 where feature points extracted from the reference B-mode image exist. Figure 12(b) shows displaced tissue T2 in the B-mode image of the second frame and feature points Cx extracted from tissue T2. Also shown is a displacement vector V indicating the displacement of tissue T2 relative to tissue T1.

[0075] FIG. 12(c) shows that the position coordinates of the feature point Cx set in the tissue T2 are corrected by subtracting the positional deviation vector V from the position coordinates of the feature point Cx.

[0076] The distance evaluation process executed by the irregularity analysis unit 22 may include a process of determining the deviation of one of the paired images from a reference B-mode image (reference ultrasound image) relative to the other, and may include a first correction process of correcting the positions of the feature points of the other image based on the determined deviation and then determining the feature point distance. The feature point contour evaluation process may include a process of determining the deviation of the other of the paired images from a reference ultrasound image relative to the other, and may include a second correction process of correcting the positions of the feature points of the other image based on the determined deviation and then determining difference information.

[0077] The ultrasound diagnostic program loaded into the processor constituting the irregularity analysis unit 22 may cause the processor to execute the first and second correction processes described above.

[0078] By correcting the positions of the feature points in this manner, even if deviations from the ideal linear transport trajectory occur when transporting the ultrasonic probe 12 in the irregularity analysis mode, appropriate positions of the feature points can be determined. [Explanation of symbols]

[0079] 10 transmitting unit, 12 ultrasonic probe, 14 subject, 16 receiving unit, 18 B-mode image generating unit, 20 image memory, 22 irregularity analysis unit, 24 analysis image generating unit, 26 display processing unit, 28 display, 30 control unit, 32 operation unit, 40-1 reference B-mode image, 40-2 B-mode image of second frame, 40-3 B-mode image of third frame, 40-n B-mode image of nth frame, 42 color map image, 50, 52 feature point outline, 100 ultrasonic diagnostic device.

Claims

1. a feature point extraction process for extracting a plurality of feature points from each of a plurality of ultrasound images acquired at different positions on the subject; an evaluation process for generating information indicating irregularities in tissues within the subject based on the feature points extracted from each of the plurality of ultrasound images; An ultrasonic diagnostic apparatus comprising: an information processing unit that executes the above.

2. The ultrasound diagnostic apparatus according to claim 1, The evaluation process includes: a process of executing a distance evaluation process for each image pair between a reference ultrasonic image, which is one of the plurality of ultrasonic images, and each of the other plurality of ultrasonic images, to obtain a feature point distance between one feature point of the two ultrasonic images and a feature point corresponding to the one feature point of the other ultrasonic image, for each feature point in the two ultrasonic images; and generating information indicating the irregularity based on the feature point distances calculated for each feature point in each of the image pairs.

3. The ultrasound diagnostic apparatus according to claim 2, The distance evaluation process includes: an ultrasound diagnostic apparatus comprising: a process for determining a deviation of one of the images in the image pair from the reference ultrasound image; and a process for correcting the position of a feature point of the other of the images in the image pair based on the determined deviation, and then determining the feature point distance.

4. The ultrasound diagnostic apparatus according to claim 2 or 3, The information processing unit determining a color for each feature point in the reference ultrasound image based on the feature point distance determined for each of the image pairs; An ultrasound diagnostic apparatus that generates an image in which local regions corresponding to each feature point in the reference ultrasound image are colored.

5. The ultrasound diagnostic apparatus according to claim 1, The evaluation process includes: determining a feature point contour defined by a plurality of feature points for each of the plurality of ultrasound images; a process of executing a feature point outer shape evaluation process for obtaining difference information between the outer shape of one feature point and the outer shape of the other feature point for each image pair of a reference ultrasound image, which is any one of the plurality of ultrasound images, and each of the other plurality of ultrasound images; and generating information indicating the irregularity based on the difference information obtained for each of the image pairs.

6. 6. The ultrasonic diagnostic apparatus according to claim 5, The feature point outer shape evaluation process includes: an ultrasound diagnostic apparatus comprising: a process for determining a deviation of one of the images in the image pair from the reference ultrasound image; and a process for correcting the positions of the feature point contours of the other image in accordance with the determined deviation, and then determining the difference information.

7. The ultrasound diagnostic apparatus according to claim 1, The characteristic points are: The ultrasonic diagnostic apparatus is characterized in that the point is a point where a feature quantity indicating a geometric feature appearing in the ultrasonic image is a maximum or a point where the feature quantity exceeds a predetermined threshold.

8. a feature point extraction process for extracting a plurality of feature points from each of a plurality of ultrasound images acquired at different positions on the subject; an evaluation process for generating information indicating irregularities in tissues within the subject based on the feature points extracted from each of the plurality of ultrasound images; An ultrasound diagnostic program characterized by causing a processor to execute the above.

9. 9. The ultrasound diagnostic program according to claim 8, The evaluation process includes: a process of executing a distance evaluation process for each image pair between a reference ultrasonic image, which is one of the plurality of ultrasonic images, and each of the other plurality of ultrasonic images, to obtain a feature point distance between one feature point of the two ultrasonic images and a feature point corresponding to the one feature point of the other ultrasonic image, for each feature point in the two ultrasonic images; and generating information indicating the irregularity based on the feature point distances calculated for each feature point in each of the image pairs.

10. 9. The ultrasound diagnostic program according to claim 8, The evaluation process includes: determining a feature point contour defined by a plurality of feature points for each of the plurality of ultrasound images; a process of executing a feature point outer shape evaluation process for obtaining difference information between the outer shape of one feature point and the outer shape of the other feature point for each image pair of a reference ultrasound image, which is any one of the plurality of ultrasound images, and each of the other plurality of ultrasound images; and generating information indicating the irregularity based on the difference information obtained for each of the image pairs.

Images