Diagnostic Support Program

Abstract

Provided is a diagnosis assisting program which is capable of displaying organ motion. This diagnosis assisting program analyzes an image of an organ of a human body and displays the analysis result, and causes a computer to execute: a process of obtaining a plurality of frame images; a process of calculating a periodic change that characterizes the condition of the organ between the frame images; a process of Fourier transforming the periodic change that characterizes the condition of the organ; a process of extracting a spectrum within a fixed band that includes the spectrum corresponding to the frequency of the organ motion from among spectrums obtained after the Fourier transformation; a process of inverse Fourier transforming the spectrum extracted from the fixed band; and a process of outputting each image after the inverse Fourier transformation.

Description

[Technical field]

[0001] The present invention relates to a technique for analyzing images of the heart and displaying the analysis results. [Background technology]

[0002] In recent years, the mortality rate from cardiac disease has been on the rise, and the need for useful and simple diagnostic techniques is growing. MRI diagnostic technology has made rapid progress, and it is now possible to perform a wide range of cardiac examinations in a short time, so MRI is becoming increasingly important in cardiac imaging diagnosis. In "cardiac MRI," tests such as "cine MRI," "perfusion," "delayed contrast," and "BB (Black Blood)" methods are performed. In particular, cine MRI has the characteristics of being unlimited in the observation range, capable of observing any cross-section, and excellent reproducibility compared to ultrasound and SPECT examinations. For this reason, it is commonly used in many medical facilities. In cine MRI, for example, data is collected from the entire left ventricle using the ECG-gated method, with "approximately 10 slices / 20 phases." Recently, it has become possible to obtain high contrast between blood and myocardium using the "Steady State method." In "cardiac function analysis," the evaluation of cardiac function using MRI can obtain accurate values ​​compared to CT, LVG (left ventriculography), and SPECT, and the need for it is also increasing.

[0003] Thus, cardiac MRI is clinically useful, and cine MRI in particular is taken at many medical facilities, but images have rarely been analyzed using software. The reason is said to be that the software used for conventional cardiac function analysis required complicated operations to extract and correct the contours of the endocardial and epicardial sides of the myocardium. In addition, the reproducibility of analysis results has been an issue regarding the tracing of the contours of the endocardial and epicardial sides of the myocardium, as it is easily affected by the individual operator. Furthermore, with the spread of cardiac MRI and the increasing number of medical facilities using MRI devices provided by multiple manufacturers, there is also the issue that it is not easy to handle the data because the names of sequences differ from one manufacturer to another.

[0004] To solve this problem, software has been provided that improves the accuracy of tracing myocardial contours and reduces the effort required for correction, even if an unintended trace is made, by automatically performing an interpolation process. This software displays images side-by-side in the MRI cardiac function analysis viewer and allows for stress-free image observation by flicking. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] https: / / www.zio.co.jp / ziostation2 / Summary of the Invention [Problem to be solved by the invention]

[0006] However, it is not easy for doctors to grasp the pathology by simply displaying MRI images side by side, as in the technology described in Non-Patent Document 1. For this reason, it is desirable to display images that correspond to the state of the heart. In other words, it is desirable to grasp the heart of the subject, a human body, and display an image showing the actual movement based on the waveform or frequency of the heart, or the change tendency of the image.

[0007] The present invention has been made in view of the above circumstances, and aims to provide a diagnostic support program capable of displaying the movement of an organ. More specifically, the present invention aims to calculate values ​​that aid diagnosis by quantifying the matching rate and other mismatch rates for the already acquired wave shape and Hz for the data of a new object to be measured, and to generate an image that aids diagnosis by imaging these values. [Means for solving the problem]

[0008] (1) In order to achieve the above object, the present application has taken the following measures. That is, a diagnostic assistance program according to one aspect of the present invention is a diagnostic assistance program that analyzes images of human organs and displays analysis results, and is characterized in that the program causes a computer to execute the following processes: acquiring a plurality of frame images, calculating periodic changes that characterize the state of the organ between the frame images, applying a Fourier transform to the periodic changes that characterize the state of the organ, extracting a spectrum within a certain band that includes a spectrum corresponding to the frequency of the movement of the organ from the spectrum obtained after the Fourier transform, applying an inverse Fourier transform to the spectrum extracted from the certain band, and outputting each image after the inverse Fourier transform.

[0009] (2) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it further includes a process of dividing an image of a human organ into a plurality of block areas and calculating changes in the image of the block areas in each of the frame images, a process of performing a Fourier transform on the changes in the image of each block area in each of the frame images, a process of extracting a spectrum within a certain band from the spectrum obtained after the Fourier transform, the spectrum including a spectrum corresponding to the frequency of the organ movement, and a process of performing an inverse Fourier transform on the spectrum extracted from the certain band.

[0010] (3) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; calculating periodic changes that characterize the state of the organ between each of the frame images; extracting pixels in the images using a digital filter that change in response to the frequency of the periodic changes that characterize the state of the organ; and outputting an image including the pixels extracted by the digital filter.

[0011] (4) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it further includes a process of dividing an image of an organ into a plurality of block areas and calculating changes in the image of the block areas in each of the frame images, and a process of extracting, for each of the block areas, pixels that change in response to the frequency of the movement of the organ in the image using a digital filter.

[0012] (5) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; calculating a periodic rate of change characterizing the state of the organ between each of the frame images; selecting a color corresponding to the periodic rate of change characterizing the state of the organ; and displaying the image on a display by applying the selected color to the rate of change of pixel values.

[0013] (6) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it further includes a process of dividing an image of an organ into a plurality of block areas and calculating a rate of change of an image of the block areas in each of the frame images, and a process of selecting a color corresponding to the rate of change of the pixel value for each of the block areas.

[0014] (7) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of the human body and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; defining an analysis range for all of the acquired frame images; dividing the analysis range into a plurality of regions using a Voronoi division technique; and performing, for each of the divided regions, any of the operations performed on periodic changes.

[0015] (8) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of the human body and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; defining an analysis range for all of the acquired frame images; dividing the analysis range into a plurality of regions; and classifying each of the divided regions based on an index of periodic change.

[0016] (9) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; calculating periodic changes that characterize the state of the organ while maintaining the absolute positional relationship of each pixel in the plurality of frame images; performing a Fourier transform on the periodic changes that characterize the state of the organ; extracting a spectrum within a certain band that includes a spectrum corresponding to the frequency of the movement of the organ from the spectrum obtained after the Fourier transform; performing an inverse Fourier transform on the spectrum extracted from the certain band; and outputting each image after the inverse Fourier transform.

[0017] (10) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; calculating periodic changes that characterize the state of the organ while maintaining the absolute positional relationship of each pixel in the plurality of frame images; extracting pixels in the images that change in response to the frequency of the periodic changes that characterize the state of the organ using a digital filter; and outputting an image including the pixels extracted by the digital filter.

[0018] (11) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; calculating periodic changes that characterize the state of the organ while maintaining the absolute positional relationship of each pixel in the plurality of frame images; selecting a color corresponding to the periodic rate of change that characterizes the state of the organ; and displaying the image on a display by applying the selected color to the rate of change of the pixel value.

[0019] (12) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it classifies a plurality of frame images into a plurality of groups, and calculates periodic changes characterizing the state of an organ while maintaining the absolute positional relationship of each pixel within the plurality of frame images belonging to each group.

[0020] (13) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it calculates changes in images of block regions in each of the frame images while maintaining the relative positional relationship of pixels representing the organ between the frame images, regardless of whether they are adjacent frames.

[0021] (14) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it further includes a process for restoring a changed transmittance of a specific area in a frame image captured by X-rays to a constant form.

[0022] (15) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it further includes a process of correcting signal values ​​in areas of a frame image captured by MRI where the MRI magnetic field is inhomogeneous, and converting the image into one that would be obtained if the magnetic field were homogeneous.

[0023] (16) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is characterized in that it calculates a change rate between each frame image, regardless of whether the frames are adjacent, from the manner of change in the entire organ, and calculates the change in the image of the specific block region based on the calculated change rate.

[0024] (17) In a diagnostic assistance program according to one aspect of the present invention, the rate of change varies depending on a position of a block region in the organ, or is constant throughout the organ.

[0025] (18) Furthermore, a diagnostic assistance program according to one embodiment of the present invention includes a process for setting a maximum outer edge when the size of the organ is maximum, a process for setting a minimum outer edge when the size of the organ is minimum, a process for calculating coefficients of the outer edges of organs of other sizes in each image using the maximum outer edge and the minimum outer edge, and a process for displaying on a graph a waveform corresponding to the coefficients of the outer edge of the organ in each image and control points of the waveform, and is characterized in that the coefficients of each image are changed by varying the position of the control points.

[0026] (19) Furthermore, a diagnostic assistance program according to an aspect of the present invention is characterized in that an average pixel value of the image of the organ is displayed on the graph.

[0027] (20) Furthermore, a diagnostic assistance program according to one aspect of the present invention is characterized in that an image of the organ is displayed juxtaposed with the graph.

[0028] (21) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; dividing the images of the human organs in each of the frame images into a plurality of block areas; calculating changes in the images of the block areas in each of the frame images; Fourier transforming the change values ​​of each of the block areas; and color-coding each area based on the composition ratio of frequency components.

[0029] (22) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that the program causes a computer to execute the following processes: acquiring a plurality of frame images; extracting, for each pixel in a specific frame image, pixels in subsequent frame images that have coordinates different from those of the respective pixels, and calculating periodic changes that characterize the state of the organ; performing a Fourier transform on the periodic changes that characterize the state of the organ; extracting, from the spectra obtained after the Fourier transform, spectra within a certain band that includes a spectrum corresponding to the frequency of the movement of the organ; performing an inverse Fourier transform on the spectrum extracted from the certain band; and outputting each image after the inverse Fourier transform.

[0030] (23) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; extracting, for each pixel in a specific frame image, pixels in subsequent frame images that have different coordinates from the respective pixels, and calculating periodic changes that characterize the state of the organ; extracting, using a digital filter, pixels that change in response to the frequency of the periodic changes that characterize the state of the organ in the images; and outputting an image including the pixels extracted by the digital filter.

[0031] (24) Furthermore, a diagnostic assistance program according to one embodiment of the present invention is a diagnostic assistance program that analyzes images of human organs and displays the analysis results, and is characterized in that it causes a computer to execute the following processes: acquiring a plurality of frame images; extracting, for each pixel in a specific frame image, pixels in subsequent frame images that have different coordinates from the respective pixels, and calculating a periodic change that characterizes the state of the organ; selecting a color that corresponds to the periodic rate of change that characterizes the state of the organ; and applying the selected color to the rate of change of the pixel value and displaying the image on a display. Effect of the Invention

[0032] According to one aspect of the present invention, it is possible to grasp the heart of a human body that is a subject, and display an image showing the actual movement based on the waveform or frequency of the heart, or the change tendency of the image. [Brief description of the drawings]

[0033] [Figure 1] 1 is a diagram showing a schematic configuration of a diagnosis support system according to an embodiment of the present invention. [Figure 2A] FIG. 1 illustrates a cross-sectional view of a heart. [Figure 2B] FIG. 1 illustrates a cross-sectional view of a heart. [Figure 2C] FIG. 1 illustrates a cross-sectional view of a heart. [Figure 2D] FIG. 2 is a diagram illustrating an example of Voronoi division. [Figure 3A] FIG. 13 is a diagram showing the change in “intensity” of a specific block and the result of Fourier analysis thereof. [Figure 3B] This figure shows the result of a Fourier transform in which frequency components close to the heartbeat are extracted, and the inverse Fourier transform of this result to show the change in "intensity" of the frequency components close to the heartbeat. [Figure 3C] FIG. 13 is a diagram showing an example of extracting a certain band from a spectrum obtained after a Fourier transform. [Figure 4] 4 is a flowchart showing an overview of image processing according to the present embodiment. [Diagram 5] 4 is a flowchart showing an overview of image processing according to the present embodiment. [Figure 6] 4 is a flowchart showing an overview of image processing according to the present embodiment. [Figure 7A] 1 is a schematic diagram showing the left lung of a human body from the front. [Figure 7B] 1 is a schematic diagram showing the left lung of a human body from the left side. [Figure 8A] 1 is a schematic diagram showing the left lung of a human body from the front. [Figure 8B] 1 is a schematic diagram showing the left lung of a human body from the left side. [Figure 9A] 1 is a schematic diagram showing the left lung of a human body from the front. [Figure 9B] 1 is a schematic diagram showing the left lung of a human body from the left side. [Figure 10A] 1 is a schematic diagram showing the left lung of a human body from the front. [Figure 10B] 1 is a schematic diagram showing the left lung of a human body from the left side. [Figure 11] FIG. 2 is a diagram showing an example of a lung field detection method according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] The present inventors have focused on the fact that there has been no practical application of technology for visualizing the movement of an organ (e.g., the myocardium of the heart) in the past, and have found that it is possible to support doctors in making diagnoses by expressing not only the movement of an organ but also the deviation of the movement of the organ and the places where the organ is not moving, and have arrived at the present invention. That is, since filtering processing has not been applied well in the past in image processing technology of the heart, a filter that extracts a targeted frequency is applied to an image of the heart, making it possible to make things that were difficult to see visible. This simplifies what previously required skill in diagnosis, and makes it possible to qualitatively characterize moving images and provide objectivity to the displayed content. In this specification, the heart is used as an example of an organ, but it goes without saying that the present invention is not limited to the heart and can be applied to various organs and blood vessels.

[0035] In the present invention, it is assumed that the movement of the myocardium is constant, and it is possible to make a judgment by comparing with previous images of the subject, by comparing the area to be analyzed with an average of the entire myocardium, by comparing with normal images, or by comparing with age-based samples.

[0036] First, the basic concept of the present invention will be described. In the present invention, for periodic changes that characterize the state of organs in the human body, for example, movements that are perceived as repeating at a certain cycle in the cross-sectional area, surface area, and volume of the heart, a certain repetition or certain movement (routine) on the time axis is captured as a wave for the whole or a certain partial range, and measured. The measurement results of the wave are (a) the wave form itself, or (b) the wave interval (frequency: Hz).

[0037] In cardiac images, there may be waves that are similarly linked at the same time. For example, the heartbeat can be approximated as follows: (Average density change in a certain rough range) ≒ (heart rate) ≒ (heart change) ≒ (electrocardiogram) ≒ (change in surface area and volume of the heart)

[0038] In the present invention, for example, it is possible to focus on and analyze the deformation of the local ventricular wall (wall thickness expansion, wall thickness contraction) of the heart, and it is possible to capture and periodically display the thickness during systole, thickness during diastole, and wall motion (the epicardium and the intima). Furthermore, it is possible to capture and periodically display the "relative wall thickness (RWT)" calculated from "(2 x posterior wall thickness) / (left ventricular end-diastolic diameter)" and the "ejection fraction" calculated from "(end-diastolic volume - end-systolic volume) / (end-diastolic volume)" of the heart.

[0039] In the present invention, any of these data or a combination of these data can be used to extract images with higher accuracy. In this case, calculations may be performed interactively many times. In this case, artifacts from the results are removed again, and functions are extracted from new data extraction waveforms, the initial base data waveforms, waveforms of other modalities, surroundings, and multiple waveforms to extract functions. In this case, the number of times may be one or multiple.

[0040] When creating base data, multiple modalities (for example, a certain amount of density, volumetric variation, cardiac movement, etc.) or multiple waveform measurements of heartbeats, etc. are used to complement each other's component extraction and improve accuracy. This makes it possible to reduce artifacts and improve accuracy based on certain predictions such as lines.

[0041] Here, "density" is translated as "density," but in an image it means the "absorption value" of a pixel in a specific area. For example, in CT, air is used as "-1000," bone as "1000," and water as "0."

[0042] In this specification, "density" and "intensity" are used separately. As mentioned above, "density" means absorption value, and in the original image of XP or XP video, air has high transparency, and the highly transparent parts are expressed as white, and air is displayed as "-1000", water as "0", and bone as "1000". On the other hand, "intensity" is a relative change from "density", for example, normalized and "converted" to the range of density or the degree of signal and displayed. In other words, "intensity" is a relative value such as brightness and emphasis in an image. While directly dealing with the absorption value of XP images, it is expressed as "density" or "change in density (Δdensity)". Then, for convenience of image expression, this is converted as described above and expressed as "intensity". For example, when displaying in color in 256 gradations from 0 to 255, it becomes "intensity". Such a distinction between terms applies to the cases of XP and CT.

[0043] On the other hand, in the case of MRI, even if you try to define air as "-1000", water as "0", and bone as "1000", the values ​​can vary greatly depending on the MRI pixel value, the type of measuring device, the person's physical condition at the time of measurement, body type, and the measurement time. In addition, the way MRI signals are collected, such as T1 weighted images, varies depending on the facility and the type of measuring device, and is not constant. For this reason, in the case of MRI, it is not possible to define "density" as in the case of XP and CT. For this reason, with MRI, relative values ​​are handled from the initial imaging stage, and are expressed as "intensity" from the beginning. And the signals that are processed are also "intensity".

[0044] This makes it possible to obtain master data. For the master data, a certain width and range of the waveform and wave Hz of the master data is extracted for a new target to be measured. For example, only heartbeat extraction or a width and range as a framework for blood vessel extraction is extracted. The waveform and Hz width are determined relatively and comprehensively based on statistics using waveform elements in other functions, artifacts such as noise, waveforms of other modalities that are thought to have other synchronicity, and reproducibility of multiple runs. Adjustment and experience are required there (machine learning can also be applied). This is because if the width and range are widened, elements of other functions begin to enter, while if it is too narrow, the elements of the function itself are stripped away, so adjustment is required for that range. For example, if there is multiple data, it is easy to specify the range, Hz, and measurement agreement width. It is also possible to estimate the axis, width, range, and Hz fluctuation and width by extracting each other's components. In other words, by overlapping multiple times, the axis setting of Hz is averaged and the optimal range of axis, width, range, and Hz is calculated by variance. At that time, the Hz (noise) of other actions is extracted, and if there is any, the degree to which that noise is not present is also measured relatively.

[0045] Next, the data of the new object to be measured is quantified based on the shape of the originally captured wave, the match rate for Hz, and other mismatch rates to calculate values ​​that will aid in diagnosis. For example, along with the elimination of noise from a pulse meter and auscultation, the match rate of the master disease waveform can be measured and the match rate of the disease waveform can be calculated, making it possible to apply the method to a diagnostic aid. In this specification, a case where the pixel value synchronization match rate can be used will be described.

[0046] [About the synchronization rate] In this specification, the tendency of image change is described as a synchronization matching rate. For example, a myocardial region is detected and divided into a plurality of block regions, and the average density (pixel value x) of the block regions in each frame image is calculated. Then, the ratio (x') of the average pixel value of the block region in each frame image to the change range (0% to 100%) from the minimum to the maximum value of the average density (pixel value x) is calculated. Meanwhile, using the ratio value (x' / y') of the ratio (y') of the change in myocardial change (y) in each frame image to the change range (0% to 100%) from the minimum position to the maximum position of the myocardium, only block regions where the ratio value (x' / y') is within a predetermined certain range are extracted.

[0047] Here, when y'=x' or y=ax (a is the coefficient of the amplitude value or density value of the myocardium), it is a perfect match. However, only the perfect match is not a meaningful value, and a value with a certain range should be extracted. Therefore, in one embodiment of the present invention, a certain range is defined as follows using logarithm (log). That is, when calculated as a percentage (%) of y=x, the perfect match of the synchronization is "log y' / x'=0". Furthermore, when extracting a range in which the synchronization matching rate is narrow (narrow mathematically), for example, "log y' / x'=-0.05 to +0.05" is defined as a range close to 0, and when the synchronization matching rate is wide (wide mathematically), for example, "log y' / x'=-0.5 to +0.5" is defined as a range close to 0. The narrower this range is, and the higher the matching value within that range, the higher the matching rate can be said to be. If the ratio value is calculated for each pixel and the number of pixels is counted, a normal distribution with a peak at the exact match is obtained for a healthy person. In contrast, the distribution of the ratio value is disrupted for a diseased person. Note that the method of determining the width using logarithms as described above is merely an example, and the present invention is not limited to this.

[0048] That is, the present invention performs "image extraction" assuming that (average of density change in a certain rough range) ≒ (heartbeat) ≒ (cardiac change) ≒ (electrocardiogram) ≒ (change in surface area and volume of the heart), and methods other than the method using logarithms can also be applied. With such a method, it becomes possible to display a frequency synchronous image.

[0049] In the case of blood vessels, a slight time delay (phase change) exists in the series of density changes (x (one waveform in the myocardium)) that occur in response to a series of cardiac contractions (y), and so it is expressed as y=a'(xt). In the case of perfect agreement, t=0, so y=x or y=a'x. Here, when extracting a range in which the synchronization agreement rate is narrow (mathematically narrow), for example, a range close to 0 is set as "log y' / x'=-0.05~+0.05", and when extracting a range in which the synchronization agreement rate is wide (mathematically wide), for example, a range close to 0 is set as "log y' / x'=-0.5~+0.5". The narrower this range is, and the higher the matching value within that range, the higher the matching rate can be said to be.

[0050] For other blood vessels, the above "part corresponding to the heart" is excluded, and the density plotted from the hilum on the central side may be used. Peripheral blood vessels can be treated in the same way.

[0051] Furthermore, the present invention can also be applied to the circulatory system. For example, the change in density of the heart is directly related to the change in density of the blood flow from the hilum to the peripheral lung field, and the series of changes in density of the heart and the hilum are propagated as they are after undergoing a kind of transformation. It is considered that this occurs with a slight phase difference due to the relationship between the change in density of the heart and the change in density of the hilum. In addition, since the change in density of the hilum etc. is directly related to the change in density of the blood flow in the lung field, it is also possible to express the synchrony by reflecting it as a rate (relationship of the coincidence rate of y ≒ x). Similarly, in the cervical vascular system, the change in density plotted in the central cardiac blood vessels in the vicinity is considered to be directly related or related with a slight phase. Then, the density fluctuates according to the background, and when it propagates, it is possible to consider it as a synchrony coincidence rate, assuming that the state of the density change is transmitted.

[0052] Here, when trying to display the amount of change and rate of change of an image as relative values ​​(Standard Differential Signal Density / Intensity) when the amount of change from the density of the heart is set to 1, it is possible to visualize the amount of change and rate of change for each of the following: (1) the image when the difference between each image is set to 1 for each image (normal assumption), (2) the ratio when the heart rate is set to 1 when the density (amount of change or rate of change) of the difference between each image is added, and further, (3) the ratio when the total density at each heart rate in multiple shots is set to 1.

[0053] In the case of 3D such as MR, the total value of the intensity (in the case of MR) or density (in the case of CT) of the heartbeat (when this is set to 1), and the difference in the intensity or density can be converted into the "peak flow volume data" of the heartbeat (even at rest or under stress), and by calculating the ratio of the intensity or density, it is possible to convert the actual movement volume and movement rate of the heart, at least when calculating "3D x time" with MRI, CT, etc. Similarly, by inputting the stroke volume, it is also possible to present an estimated value that converts the distribution in the "capillary phase" of the "flow" of the lung field into the distribution and volume of the peripheral pulmonary blood flow.

[0054] In other words, (average density change over a certain rough range) ≒ (heart rate) ≒ (heart change) ≒ (electrocardiogram) ≒ (change in surface area and volume of the heart), and when extracting only the amount of change of 10% or 20% on one image, it is possible to obtain an estimate by calculating (number of all images) x (amount of change at that time).

[0055] Then, for the data of the new object to be measured, the shape of the originally captured wave, the agreement rate with respect to Hz, and other discrepancies are visualized to calculate images that will assist in diagnosis. For example, the difference between normal swallowing and the patient's swallowing is visualized, and the difference between the previous action and the current action is shown. For example, the way the feet are moved when walking, the swing change, and the difference.

[0056] The amount of change extracted is visualized and depicted on an image. This is the cardiac function analysis and vascular (blood flow) analysis described below. The rate of change in the myocardium is then visualized. At this time, artifacts from the results are again removed, and function extraction may be performed by extracting from new data extraction waveforms, the initial base data waveform, waveforms from other modalities, surrounding waveforms, and multiple waveforms. Methods for removing artifacts will be described later.

[0057] In addition, there are cases where the feature amount is obtained by excluding the change components extracted from the above-mentioned extracted ones. For example, when grasping the movement of the abdominal intestine, the influence of respiration and the influence of blood vessels from the abdomen are excluded to extract the movement of the abdominal intestine.

[0058] In addition, the rate of change due to this extraction can be used to correct images that take a certain amount of time to be captured (CT, MRI, special X-rays, PET / scintigraphy, etc.) to provide clearer, more accurate images. For example, this is effective for ascending aorta heart correction, cardiac morphology correction, correction of bronchial blur, evaluation of the thoracic cage circumference, and imaging in situations where the patient cannot hold their breath (it can take several minutes to capture a patient image).

[0059] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration of a diagnosis support system according to this embodiment. This diagnosis support system exerts a specific function by making a computer execute a diagnosis support program. The basic module 1 is composed of a cardiac function analysis unit 3, a blood flow analysis unit 5, other blood flow analysis units 7, a Fourier analysis unit 9, a waveform analysis unit 10, and a visualization and digitization unit 11. The basic module 1 acquires image data from a database 15 via an input interface 13. For example, images according to DICOM (Digital Imaging and COmmunication in Medicine) are stored in the database 15. The image signal output from the basic module 1 is displayed on a display 19 via an output interface 17. Next, the function of the basic module according to this embodiment will be described. Note that the input image is not limited to the database 15, and it is also possible to input an image from another external device via the input interface 13, or to input the image manually.

[0060] [Refinement of dynamic part detection] The contrast of dynamic parts such as the lung field, thorax, and heart may not be uniform along the line. In such cases, the shape of the dynamic parts can be detected more accurately by changing the threshold value used for noise removal and performing the detection process multiple times. For example, in the left lung, the contrast of the diaphragm line tends to weaken as it goes deeper into the human body. There is also the fact that the apex and base of the heart move less, while the center of the heart moves more. In such cases, the remaining left half of the diaphragm and parts of the heart with large and small movements can be detected by changing the threshold value used for noise removal or multiplying the pixel values ​​by different coefficients. By repeating this process multiple times, it is possible to detect the shape of the entire diaphragm and the shape of the entire heart. In this way, this method makes it possible to quantify not only the position of the diaphragm, but also the rate and amount of change of lines and surfaces for the shapes of the thorax, heart, and dynamic parts, which can be useful for new diagnoses.

[0061] The position or shape of the diaphragm or heart detected in this way can be used for diagnosis. That is, the coordinates of the diaphragm or heart can be graphed, and the coordinates of the thorax, diaphragm, and heart can be calculated using the curves (surfaces) or lines calculated as described above. Also, the heartbeat, blood vessel beat, and "density" of the lung field can be graphed as positions and coordinates corresponding to the cycle. Such a method can also be applied to dynamic parts that are linked to breathing and heartbeat.

[0062] This method allows us to measure not only the Hz of inhalation, exhalation, systole, and diastole, but also the frequency of the diaphragm or other moving parts linked to breathing, and the frequency (Hz) of the moving parts of the heart, in the frequency band corresponding to the change. When extracting the spectrum of the BPF (band pass filter), it is possible to set the BBF according to the state of breathing and the heart in a certain range, to change the axis of the BPF position in the "reconstruction phase" of breathing and the heart, and to create an optimal state, and to create a variable BPF that combines this. This makes it possible to provide images that correspond to the rhythm of breathing, such as when breathing slows or stops (Hz=0), or when the heart temporarily fibrillates (extremely high frequency) or stops (Hz=0).

[0063] Also, the overall frequency of exhalation or inhalation may be calculated based on the proportion of the respiratory element (a respiratory element including all or part of exhalation or inhalation) to the entirety of exhalation or inhalation. Similarly, the frequency of the systole or diastole, the frequency element, and the whole may be calculated based on the proportion of the cardiac dynamic element (a cardiac dynamic element including all or part of the systole or diastole) to the systole or diastole, one heart beat, and the whole beat measured. In addition, the detection of the diaphragm or the heart may be performed multiple times and a signal or waveform with a stable signal or waveform may be selected. As described above, it is possible to calculate at least one frequency of the respiratory element or the heartbeat element, or to calculate a frequency representing the heartbeat, from the detected position or shape of the diaphragm or the heart, or the position or shape of a dynamic part linked to breathing. If the position or shape of the diaphragm, the heart, or a dynamic part can be grasped, it is possible to grasp the frequency of the respiratory element or the heartbeat element, or the heartbeat. According to this method, even if a part of the waveform is separated, the waveform after that can be tracked. Therefore, even if the frequency of the respiratory or cardiac components changes during the process, it is possible to track the original respiratory or cardiac components. In addition, although the heartbeat may suddenly change, the same can be applied to the cardiovascular system and organs related to the cardiovascular waveform.

[0064] [Lung field detection] In the present invention, as one aspect of the above-mentioned "refinement of dynamic area detection," it is possible to refine lung field detection. In this process, after setting the maximum and minimum lung fields, the other lung fields are calculated using those values. FIG. 11 is a diagram showing an example of a lung field detection method in the present invention. In this method, "coefficients of each image" are expressed using "B-spline curves." In FIG. 11, waveform X represents "coefficients of each image L showing lung fields," and from left to right, it is the coefficient of the first image, the coefficient of the second image, and so on. When control point Y in FIG. 11 is moved, "coefficients of each image" change smoothly. In this way, in the present invention, it is possible to directly edit the graph of coefficients. In FIG. 11, "gray broken line Z" represents "average pixel value of each image." When photographed under optimal conditions, the change in the size of the lung field and the change in the average pixel value coincide. Here, this average pixel value can be smoothed by curve fitting and used directly as a "coefficient." It is also possible to apply the same to the heart and other organs involved in the frequency of the cardiovascular system.

[0065] [Cardiac function analysis] FIG. 2 is a cross-sectional view showing a schematic configuration of the heart. "Cardiac function" is generally defined as "the pumping function of the left ventricle that circulates blood throughout the body." In "ischemic heart disease," especially "myocardial infarction," cardiac function analysis is important for predicting the prognosis of patients. For example, when the value of the left ventricular ejection fraction (EF) decreases, the output of the heart as a pump decreases, and it becomes impossible to pump sufficient blood to the entire body. Other cardiac functions include the left ventricular end-diastolic volume (EDV), left ventricular end-systolic volume (ESV), stroke volume (SV), cardiac output (CO), and cardiac index (CI). For local evaluation of the myocardium, a "Bull's eye map" showing wall thickness, wall motion, wall thickness change rate, etc. is used, as shown in FIG. 2B and FIG. 2C, which are cross sections taken along a plane perpendicular to the axis A in FIG. 2A. This "Bull's eye map" is an image displayed in which the cross-section of the apex is placed in the center of a circle, with short-axis cross-sectional images arranged in concentric circles around it, and the cross-section of the base is placed on the outermost side.

[0066] In this embodiment, the "Bull's eye map" is used and the period of cardiac movement is analyzed based on the following index. That is, the period of cardiac movement is analyzed using density / intensity in a certain region in the cardiac region. In addition, a range consisting of a certain volume density / intensity measured in a region with high permeability to X-rays (and multiple types of modalities such as CT and MRI), data obtained from other measurement methods such as spirograms, and external input information may be used. It is desirable to compare the analysis results for each heartbeat and analyze trends from multiple data to increase the accuracy of the data. It is also possible to identify the edge of the heart and obtain the frequency based on the change in the edge of the heart. It is also possible to identify the edge of the lung field and obtain the frequency from the movement of the edge.

[0067] [Vascular pulse analysis] In this embodiment, the vascular pulse is analyzed based on the following indexes. That is, the heart, pulmonary hilum position, and major blood vessels are identified from the measurement results of other modalities such as an electrocardiogram or a pulse meter, or from the lung contour, and the vascular pulse is analyzed using the change in density / intensity of each part. It is also possible to manually plot on an image and analyze the change in density / intensity of the target part. It is also possible to use the heartbeat element obtained from the heartbeat or vascular pulse. It is desirable to compare the analysis results for each beat and analyze the trend from multiple data to increase the accuracy of the data. It is also possible to increase the accuracy of the density / intensity extraction of each part by performing it multiple times or for a certain range. There is also a method of inputting the cardiovascular pulse frequency or frequency band.

[0068] [Identification of the cardiac region] Images are extracted from the database (DICOM), and the cardiac region (particularly the myocardium) is automatically detected using the cardiac function analysis results. Next, the myocardium is divided into multiple block regions, and changes in each block region are calculated. Here, the size of the block region may be determined according to the imaging speed. When the imaging speed is slow, it is difficult to identify a corresponding part in the next frame image of a certain frame image, so the block region is made large. On the other hand, when the imaging speed is fast, the number of frame images per unit time is large, so tracking is possible even if the block region is small. In addition, the size of the block region may be calculated according to which timing of the cardiac movement cycle is selected. Here, it may be necessary to correct the deviation of the myocardial region. In that case, the cardiac movement is identified, and the relative position of the cardiac contour is grasped, and a relative evaluation is made based on that movement. Note that if the block region is too small, flickering of the image may occur. To prevent this, the block region needs to have a certain size.

[0069] [Create Block Region] Next, a method of dividing the myocardium into a plurality of block regions will be described. Figures 2B and 2C are diagrams showing a method of dividing the myocardium radially from the center of the heart. The region of the heart should be relatively evaluated based on the movement of the heart and the positional relationship of blood vessels, the relative position of the heart's contour, and the movement. For this reason, in the present invention, after automatically detecting the contour of the heart, the region of the myocardium is divided into a plurality of block regions, and the value of the change in the image (pixel value) contained in each block region is averaged. As a result, even if the shape of the heart changes over time, it is possible to track the change over time in the region of interest.

[0070] On the other hand, if the region of the heart is divided into blocks without identifying it, the region of interest may move out of the region of the heart due to changes in the heart over time, resulting in a meaningless image. There is also a method of inputting the heart rate or frequency band. These methods can also be applied to three-dimensional stereoscopic images. By making the pixels in a 3D stereoscopic image constant, it is possible to calculate region division for 3D as well. This relative evaluation based on the movement of relative positions may be performed between adjacent frame images, or may be performed for integer multiples, such as every two or three images. Also, several images may be grouped together and processing may be performed for each group.

[0071] FIG. 7A is a schematic diagram showing the left lung of a human body from the front, and FIG. 7B is a schematic diagram showing the left lung of a human body from the left side. Both FIG. 7A and FIG. 7B show the lungs in the inhalation state, that is, the state of breathing in. FIG. 8A is a schematic diagram showing the left lung of a human body from the front, and FIG. 8B is a schematic diagram showing the left lung of a human body from the left side. Both FIG. 8A and FIG. 8B show the lungs in the exhalation state, that is, the state of breathing out. As shown in these figures, the shape of the lung field changes greatly during breathing, but the rate of change of the lung field on the diaphragm side is large, and the rate of change of the lung field on the opposite side of the diaphragm is small. In the present invention, the position of each region in the lung field is changed according to this rate of change. This makes it possible to perform a relative evaluation based on the relative positional relationship of each region in the lung field region. In addition, the relative positional relationship can be displayed at a constant rate of change in the lung field region based on the rate of change (for example, the average rate of change) of the lung field, or the rate of change in the lung field region can be adaptively changed according to the distance from the diaphragm. In this way, by using the rate of change in the lung field region, it is possible to display an image synchronized with the respiratory cycle.

[0072] FIG. 9A is a schematic diagram showing the left lung of a human body from the front, and FIG. 9B is a schematic diagram showing the left lung of a human body from the left side. Both FIG. 9A and FIG. 9B show the lungs in the inhalation state, that is, the state of breathing in. FIG. 10A is a schematic diagram showing the left lung of a human body from the front, and FIG. 10B is a schematic diagram showing the left lung of a human body from the left side. Both FIG. 10A and FIG. 10B show the lungs in the exhalation state, that is, the state of breathing out. For example, as shown in FIG. 9A and FIG. 9B, assume that a marker P1 is plotted at a certain location in the lung field region in the inhalation state. If the marker P1 is a fixed point determined by two-dimensional coordinates, the coordinates do not change even in the exhalation state, so that the marker P1 exists in the same position as shown in FIG. 10A and FIG. 10B. On the other hand, in the present invention, as described above, the evaluation is performed based on the relative positional relationship with respect to the entire lung field region, so that in the exhalation state, the marker P1 moves to the position of the marker P2, not to the position of the marker P1. Using the vector at this time, it is also possible to evaluate the point plotted during inspiration and the point whose destination during expiration has been identified.

[0073] When dividing the region, it is possible to apply Voronoi division (Thiessen division). As shown in FIG. 2D, this Voronoi division is a method of drawing a perpendicular bisector to a line connecting adjacent kernel points to divide the nearest region of each kernel point. By applying such Voronoi division, it is possible to reduce the calculation time. Furthermore, when drawing a line connecting adjacent kernel points, weighting may be applied according to the analysis target. For example, when dividing the region of the pulmonary artery, the weighting may be high in the thick part and low in the thin part. This reduces the processing load and enables division according to the analysis target. The multiple block regions generated by division may be classified based on an index such as a change in pixel value (periodic change).

[0074] After dividing the region into a plurality of block regions in this way, the change in the image of each block region is calculated based on the relative position of each block region and a dynamic part such as the heart. Here, the signal difference may be calculated not only for the range of the mass itself as pixels as one unit, but also for a range smaller than the mass, or for a large range larger than the mass and surrounding it. Furthermore, the range in the vertical direction may be enlarged in the vicinity of the diaphragm, the range in the horizontal direction may be enlarged in other dynamic parts, the shape of the range may be deformed, or the pixel region may be linked. In addition, after calculating one difference or multiple differences, it is desirable to redefine the shape of the mass again in accordance with the shape of the entire lung field or heart. For example, after processing from the first to the second image, the shape of the mass may be created again in the second image in accordance with the shape, and compared with the second to the third image.

[0075] In the above explanation, the "relative positional relationship" considering the movement of the organs has been explained, but the present invention is not limited to this, and it is also possible to process images while maintaining the "absolute positional relationship of each pixel" in multiple frame images. The "absolute positional relationship of each pixel" refers to the relationship between pixels having coordinates specified based on a two-dimensional coordinate axis when a two-dimensional coordinate axis is defined on the frame image. In other words, this is a method of processing pixels while keeping the pixel of interest unchanged. Processing while maintaining the "absolute positional relationship of each pixel" is premised on multiple frame images, but the number of frame images is not specified. The multiple frame images may be classified into multiple groups, and each group may contain an equal number of frame images, or each group may contain a different number of frame images.

[0076] That is, multiple frame images are classified into multiple groups, and the change in pixel value of an organ, the change in distance from the center to the outer edge of an organ, or the change in volume of an organ is calculated while maintaining the absolute positional relationship of each pixel in multiple frame images belonging to each group. This makes it possible to treat pixel values ​​as equal even if they change slightly, thereby reducing the amount of data and the number of processing steps.

[0077] Furthermore, a positional relationship that is neither a relative positional relationship nor an absolute positional relationship can be conceived. That is, a point P having a specific coordinate can be determined in a specific frame image, and another point Q having a coordinate different from the specific point can be determined in the next or subsequent frame image, but in this case, the magnitude of the vector PQ is assumed to be small relative to the movement of the organ. Furthermore, another point R having a coordinate slightly different from that of point Q is determined in the next or subsequent frame with respect to point Q. By repeating this operation, other points having a slight deviation from the specific point P are extracted for each frame image, and this embodiment is applied. Specifically, a plurality of frame images are obtained, and for each pixel in a specific frame image, pixels having coordinates different from the respective pixels in the next or subsequent frame images are extracted, and periodic changes that characterize the state of the organ are calculated.

[0078] Then, the periodic change characterizing the state of the organ is Fourier transformed, and from the spectrum obtained after the Fourier transform, a spectrum within a certain band including a spectrum corresponding to the frequency of the movement of the organ is extracted, and the spectrum extracted from the certain band is inverse Fourier transformed, and each image after the inverse Fourier transform is output. Alternatively, pixels in the image that change corresponding to the frequency of the periodic change characterizing the state of the organ may be extracted by a digital filter, and an image including the pixels extracted by the digital filter may be output. Alternatively, a color corresponding to the periodic rate of change characterizing the state of the organ may be selected, and the image may be displayed on a display by applying the selected color to the rate of change of pixel value. This makes it possible to express the movement of the organ.

[0079] Next, artifacts are removed and the image data is interpolated. In other words, if bones or other objects are included in the analysis range, they will appear as noise, so it is desirable to remove the noise using a noise-cutting filter. In X-ray images, air is usually represented as -1000 and bone as 1000, so areas with high transparency have low pixel values ​​and are displayed as black, and areas with low transparency have high pixel values ​​and are displayed as white. For example, when pixel values ​​are expressed in 256 gradations, black is 0 and white is 255.

[0080] In the area of ​​the heart, the pixel value of the X-ray image is high in the area where blood vessels and bones are present, and the X-ray image becomes white because X-rays do not easily penetrate there. The same can be said for CT and MRI. Here, based on the results of the cardiac function analysis, it is possible to eliminate artifacts by interpolating data using values ​​of the same phase based on the waveform per heartbeat. In addition, when "coordinate differences," "extreme fluctuations in pixel values," and "abnormally high frequencies and densities" are detected, cutoffs are performed for them, and the remaining obtained images may be used to calculate the Hz of the heart's movement and adjust the myocardial area by identifying a continuous and smooth wave shape using, for example, the least squares method. In addition, when overlapping images, there are two methods: (1) overlapping the acquired comparison images obtained by acquiring one image before and after the other with the same coordinates, and (2) acquiring one image before and after the other as the base, and then expanding the image relatively to overlap the relative position information on the base. By using the above methods, it is possible to correct the shape of the heart area and correct changes in the image of the block area.

[0081] Here, we will explain "reconstruction" on the time axis. For example, if the inhalation time of 15 f / s is 2 seconds, 30+1 images are obtained. In that case, "reconstruction" of 10% can be performed by simply overlapping three images at a time. In that case, for example, if 0.1 seconds is 10% and only photos of 0.07 seconds and 0.12 seconds are acquired, "reconstruction" of 0.1 seconds is required. In that case, the intermediate value (average of both) of the images around 10% is given and "reconstruction" is performed. It is also possible to capture it on the time axis and change the coefficient according to the ratio of that time. For example, if there is a difference in the time axis and there is no value for 0.1 seconds of shooting, but there are shooting times of 0.07 seconds and 0.12 seconds, "reconstruction" can be performed by recalculating "(the value of that 0.07 seconds) x 2 / 5 + (the value of 0.12 seconds) x 3 / 5". It is preferable to perform calculations with a certain thickness, such as 10% to 20% reconstruction or 10% to 40% reconstruction, including 0 to 100% of the "Maximum Differential Intensity Projection". In this way, it is possible to perform "reconstruction" at a rate of one heartbeat even for parts that are not photographed. It is also possible to perform "reconstruction" in the same way for a series of movements linked to the heart, blood flow, and other movements.

[0082] [Fourier analysis] Based on the heart movement cycle and blood vessel pulse cycle analyzed as described above, the density / intensity value of each block region and the amount of change are subjected to Fourier analysis. FIG. 3A is a diagram showing the intensity change of a specific block and the result of Fourier analysis of the same. FIG. 3B is a diagram showing the Fourier transform result of extracting frequency components close to the heartbeat, and the intensity change of the frequency components close to the heartbeat by inverse Fourier transforming the same. For example, when the intensity change of a specific block is Fourier transformed (Fourier analysis), the result shown in FIG. 3A is obtained. Then, when the frequency components close to the heartbeat are extracted from the frequency components shown in FIG. 3A, the result shown on the right side of the paper in FIG. 3B is obtained. By inverse Fourier transforming this, the intensity change in tune with the change in the heartbeat can be obtained, as shown on the left side of the paper in FIG. 3B.

[0083] Here, when performing an inverse Fourier transform on a spectrum consisting of frequency components, it is possible to take into account both the frequency elements (heartbeat, cardiovascular beat frequency) identified from the density of the heartbeat or blood flow and the bandwidth of the spectrum (BPF may also be used), or to perform the inverse Fourier transform based on either of these elements.

[0084] When performing the Fourier transform, it is possible to use the AR method (Autoregressive Moving Average model) to shorten the calculation time. In the AR method, there is a method that uses the Yule-Walker equation or Kalman filter in the autoregressive moving average model, and the calculation can be supplemented by using the Yule-Walker estimates derived therefrom, the PARCOR method, and the least squares method. This makes it possible to obtain images closer to real time more quickly, assist in calculations, and correct artifacts. This type of Fourier analysis makes it possible to extract and display the image characteristics in each block area.

[0085] Here, the image change of each block region in each frame image is Fourier transformed, and from the spectrum obtained after the Fourier transform, a spectrum within a certain band including a spectrum corresponding to the period of the heart movement can be extracted. FIG. 3C is a diagram showing an example of extracting a certain band from the spectrum obtained after the Fourier transform. The frequency f of the spectrum of the composite wave has a relationship of "1 / f=1 / f1+1 / f2" with each of the frequencies f1 (heartbeat component) and f2 (pathological blood flow component) that are the source of the synthesis, and the following method can be adopted when extracting the spectrum.

[0086] (1) Extract the area with a high spectral ratio of heartbeat. (2) A spectrum is extracted by dividing the spectrum peak corresponding to the heart rate / pathological blood flow midway between the peaks of multiple composite waves in the vicinity of the peak. (3) The spectrum is extracted by dividing the spectrum by the peak corresponding to the heart rate / pathological blood flow and the valleys of the spectra of multiple composite waves in the vicinity. (4) A spectrum within a certain bandwidth may be extracted from the heartbeat component (blood flow component). In this case, a spectrum consisting of multiple overlapping spectra is obtained, but each spectrum can be restored by separating each component.

[0087] As described above, the present invention does not use a fixed BPF, but extracts a spectrum within a certain band including a spectrum corresponding to the period of the heart's movement.Furthermore, the present invention can extract a spectrum within a certain band including a frequency other than the heart's movement obtained from a frame image (for example, the density / intensity of each part, the heartbeat element obtained from the heartbeat or vascular beat), or a spectrum corresponding to a frequency input from the outside by an operator (for example, a spectrum model), from the spectrum obtained after Fourier transform.

[0088] Here, if there are only two components (heartbeat, pathological blood flow), the spectrum of the composite wave is 50% + 50%, and if there are three components, the distribution is 1 / 3 each. Therefore, the spectrum of the composite wave can be calculated to some extent from the spectrum components and their heights, i.e., what percentage of the spectrum is the heartbeat component and what percentage is the pathological blood flow component. It is possible to extract the spectrum where the percentage (%) is high. In other words, the ratio of the pathological blood flow component / heartbeat component to the composite wave component is calculated, and the high spectrum value of the pathological blood flow component / heartbeat component is calculated and extracted. In addition, in identifying the diaphragm, etc., from data obtained from the frequency of the heartbeat and cardiovascular system, there are cases where only the spectrum corresponding to the area where the Hz (frequency) is relatively constant, that is, the area where the Hz change is small, or the overlapping thereof is extracted. In addition, when determining the spectrum band, there are cases where the spectrum band is determined in the range where the Hz change occurs and the surrounding area. As a result, it is possible to extract not only the spectrum that completely matches the cycle of the heart movement or the cycle of the blood vessels, but also the spectrum that should be taken into consideration, which can contribute to image diagnosis.

[0089] It is known that "heartbeat" and "breathing" are included in a specific frequency band. For this reason, it is possible to use a filter such as "0 to 0.5 Hz (respiratory rate 0 to 30 times / min)" for breathing, and "0.6 to 2.5 (heartbeat / pulse rate 36 to 150 times / min) Hz" for the circulatory system, and to specify the respiratory frequency and the circulatory system frequency in advance using this filter. This makes it possible to display a frequency synchrony image. This is because there are cases where density changes in breathing (lungs) are picked up when acquiring density changes in the heart, and where density changes in the lungs are picked up when acquiring density changes in the heart.

[0090] [Waveform analysis] Waveform analysis is performed on heart, blood vessel, brainwave, and other tests that are recognized as having a certain waveform. This includes repeated movements in a certain state, such as leg movements. In addition, the Hz of repeated movements is superimposed to analyze whether they have the same tendency. The waveform data is compared and the agreement rate of the two data is calculated. The data after Fourier analysis is then compared.

[0091] [Digital Filter] It is also possible to use a digital filter instead of the above-mentioned Fourier analysis. A digital filter performs conversion between the time domain and the frequency domain based on the "Fast Fourier Transform and Inverse Fast Fourier Transform", which are mathematical algorithms that extract the frequency components of a signal, in order to adjust the frequency components of the signal. This makes it possible to obtain the same effect as the above-mentioned Fourier analysis.

[0092] [Visualization and quantification] The results of the analysis as described above are visualized and quantified. As a standard uptake, the values ​​may be displayed relatively / logarithmically with the average value set to 1 from the density / intensity of the entire measured cardiac area. In addition, in order to use only the direction of blood flow, changes in a specific direction may be extracted. This makes it possible to extract only data of a meaningful method. Using the results of identifying the cardiac area, pseudocoloring is performed following changes in the analysis range. In other words, the analysis results of each individual (subject) are fitted to a relative area along a specific shape (minimum, maximum, average, median) that matches the phase. In addition, the results of multiple analysis are transformed into a specific shape / phase that allows comparison.

[0093] Furthermore, when creating a "standard heart," the results of the above-mentioned analysis of cardiac movement are used to calculate the relative positional relationships within the heart (myocardium). The "standard heart" is created using a line that is a comprehensive average of the outline lines and density of the hearts of multiple patients. This concept is not limited to the heart, but can also be applied to lungs (standard lungs) and other organs (standard organs). For example, it is possible to create "organ models" by age, sex, country, and degree of disease.

[0094] In addition to the changes in pixel values ​​of the heart as described above, it is also possible to calculate the change in the distance from the center of the heart to the myocardium (distance L shown in Figures 2B and 2C) and perform Fourier analysis. It is also possible to calculate the change in the volume of the heart and perform Fourier analysis.

[0095] Once the "standard heart" has been created, it becomes possible to present the synchrony, agreement rate, and discrepancy rate in numerical form (display of frequency synchrony image) as described above. Also, deviations from the normal state can be displayed. According to this embodiment, by performing Fourier analysis, it becomes possible to discover new disease possibilities, compare with the normal self, compare hands and feet, and compare with the opposite hand and foot. Furthermore, it becomes possible to grasp what is wrong with the way the feet move, swallowing, etc. by quantifying synchrony. It is also possible to determine whether a sick person has changed after a certain period of time has passed, and if so, to compare before and after the change.

[0096] [Drawing a heart] In this specification, a method is adopted in which the contour of the heart is provisionally drawn using a combination of Bezier curves and straight lines, and the heart is adjusted so as to increase the degree of matching. For example, if the contour of the heart is expressed by four Bezier curves and one straight line, the contour of the heart can be drawn by determining five points on the contour of the heart and four control points. By shifting the positions of the points to draw multiple contours of the heart, and evaluating the degree of matching using conditions such as "the total density value within the contour is maximized" and "the difference between the total density of several pixels inside and outside the contour is maximized," the contour of the heart can be detected with high accuracy. It is also possible to extract points close to the outer edge by contour extraction using classical binarization, and adjust the control point position of the Bezier curve using the least squares method, etc. It is also possible to apply the above method to other organs as "organ detection," and is not limited to the heart. It is also applicable not only to planar images, but also to three-dimensional images (3D images). By defining the equation of the curved surface and setting its control points, it is possible to estimate that an object surrounded by multiple curved surfaces is an organ.

[0097] [Cardiac function analysis using Fourier analysis] Next, cardiac function analysis using Fourier analysis according to this embodiment will be described. FIG. 4 is a flow chart showing an outline of cardiac function analysis according to this embodiment. The basic module 1 extracts DICOM images from the database 15 (step S1). Here, at least a plurality of frame images included in one cardiac beat are acquired. Next, in each acquired frame image, a period of cardiac movement is specified using a change in pixel value, for example, a change in density (density / intensity), at least in a certain region of the myocardium (step S2). Next, a region of the heart (myocardium) is detected (step S3), and the detected myocardium is divided into a plurality of block regions (step S4). Here, as described above, the myocardium is divided radially from the center of the heart using Voronoi division (Thiessen division). Then, the change in pixel value of each block region in each frame image is calculated (step S5). Here, the value of the change in each block region is averaged and expressed as one data.

[0098] It is also possible to display the pixels somewhat ambiguously and in a blurred state to display the whole image. In particular, in the case of blood vessels, low signal values ​​are mixed in among high signal values, but as long as only the high signal values ​​can be roughly grasped, it is okay for the whole image to be ambiguous. For example, in the case of blood flow, only signals above a threshold value may be extracted. Specifically, if the numbers in the following table are taken as one pixel and the middle value is obtained, the proportion of the middle value is obtained and averaged within one pixel, allowing a smooth expression between adjacent pixels. [Table 1]

[0099] It is also possible to perform noise removal by cutoff on the change value within each block region. Next, Fourier analysis is performed on the density / intensity value of each block region and the amount of change based on the cycle of the heart movement (step S6). This makes it possible to extract and display the image characteristics of each block region.

[0100] Here, from the spectrum obtained after the Fourier transform, it is possible to extract a spectrum within a certain band including a spectrum corresponding to the cardiac cycle. Here, the frequency f of the spectrum of the composite wave has a relationship of "1 / f=1 / f1+1 / f2" with each of the frequencies f1 and f2 that are the source of the synthesis, and the following method can be used to extract the spectrum.

[0101] (1) Extract the area with a high spectral ratio of cardiac motion. (2) The spectrum is extracted by dividing the spectrum by the midpoint between the peak corresponding to the heart rate / blood flow and the peaks of multiple composite waves in the vicinity. (3) The spectrum is extracted by dividing the spectrum by the peak corresponding to the heart rate / blood flow and the valleys of the spectra of multiple composite waves in the vicinity.

[0102] Here, if there are only two components in the spectrum of the composite wave (heartbeat, blood flow), the ratio is 50% + 50%, and if there are three components, the ratio is 1 / 3 each. Therefore, the spectrum of the composite wave can be calculated to some extent from the components and height of the spectrum, i.e., what percentage is the spectrum of the heartbeat component and what percentage is the spectrum of the blood flow component. It is possible to extract the spectrum where the ratio (%) is high. In other words, the ratio of the blood flow component / heartbeat component to the composite wave component is calculated, and the high spectral value of the blood flow component / heartbeat component is calculated and extracted.

[0103] Next, noise is removed from the results obtained by the Fourier analysis (step S7). Here, the above-mentioned cutoff and removal of artifacts can be performed. The above operations from step S5 to step S7 are performed one or more times, and it is determined whether or not they are completed (step S8). If they are not completed, the process transitions to step S5, and if they are completed, the results obtained by the Fourier analysis are displayed on the display as a pseudo-color image (step S9). Note that a black and white image may also be displayed. In this way, by repeating multiple cycles, the accuracy of the data may be increased. This makes it possible to display the desired moving image. Also, the image displayed on the display may be modified to obtain the desired moving image.

[0104] In addition to the above-described division process of the myocardium in steps S4 and S5, it is also possible to perform Fourier analysis by calculating the change in distance from the center of the heart to the myocardium instead of steps S4 and S5. Furthermore, it is also possible to perform Fourier analysis by calculating the change in the volume of the heart instead of steps S4 and S5.

[0105] [Cardiac function analysis using digital filters] Next, the functional analysis using the digital filter according to this embodiment will be described. FIG. 5 is a flow chart showing an outline of the cardiac function analysis according to this embodiment. Steps S1 to S5 and steps S7 to S9 are the same as the above-mentioned "cardiac function analysis using Fourier analysis", and therefore the description will be omitted. In step T1 of FIG. 5, digital filter processing is performed (step T1). The digital filter performs conversion between the time domain and the frequency domain based on "fast Fourier transform and inverse fast Fourier transform", which are mathematical algorithms for extracting the frequency components of a signal, in order to adjust the frequency components of the signal. This makes it possible to obtain the same effect as the above-mentioned Fourier analysis.

[0106] [Cardiac function analysis using synchronization agreement rate] Next, cardiac function analysis using the synchronization agreement rate according to this embodiment will be described. FIG. 6 is a flow chart showing an outline of cardiac function analysis according to this embodiment. Steps S1 to S5 and steps S7 to S9 are the same as the above-mentioned "cardiac function analysis using Fourier analysis", so their explanations will be omitted. In step R1 of FIG. 6, the synchronization agreement rate is analyzed (step R1). That is, after detecting the region of the heart (myocardium) (step S3) and dividing the myocardium into a plurality of block regions (step S4), the average density (pixel value x) of the block region in each frame image is calculated, and the ratio (x') of the average pixel value of the block region in each frame image to the change range (0% to 100%) from the minimum value to the maximum value of the average density (pixel value x) is calculated (step S5). Meanwhile, the ratio value (x' / y') of the change (y) of the heart in each frame image to the change range (0% to 100%) from the minimum value to the maximum value of the surface area (or volume) of the heart to the ratio (y') is calculated (step S5). Using these, it is possible to extract only block regions whose ratio values ​​(x' / y') are within a predetermined range (step R1).

[0107] Here, when y'=x' or y=ax (a is the coefficient of the amplitude value of the surface area or volume of the heart or the density value), it is a perfect match. However, not only the perfect match is a meaningful value, and a value with a certain range should be extracted. Therefore, in one embodiment of the present invention, a certain range is defined as follows using logarithm (log). That is, when calculated as a percentage (%) of y=x, the perfect match of synchronization is "log y' / x'=0". Furthermore, when extracting a range in which the synchronization match rate is narrow (narrow mathematically), for example, "log y' / x'=-0.05 to +0.05" is defined as a range close to 0, and when the synchronization match rate is wide (wide mathematically), for example, "log y' / x'=-0.5 to +0.5" is defined as a range close to 0. The narrower this range is, and the higher the matching value within that range, the higher the match rate can be said to be. If the ratio value is calculated for each pixel and the number of pixels is counted, a normal distribution with a peak at the exact match is obtained for the heart of a healthy person. In contrast, for a person with a disease, the distribution of the ratio values ​​collapses. Note that the above method of determining the width using logarithms is merely an example, and the present invention is not limited to this. In other words, the present invention performs "image extraction" by assuming (average of density changes in a certain rough range) ≒ (heartbeat) ≒ (changes in the heart) ≒ (electrocardiogram) ≒ (changes in the surface area and volume of the heart), and methods other than the method using logarithms are also applicable.

[0108] In addition, when considering in 3D, it is possible to measure the "partial cardiac surface area", "partial cardiac volume", and "blood flow rate" in each region from the ratio of the heart rate, surface area or volume of the heart, cardiac output, and central blood flow measured with another device. If it becomes possible to measure the cardiac surface area, cardiac volume, cardiac output, and central blood flow as quantitative measurements with other modalities, it becomes possible to estimate the estimated functional amount from the amount and its ratio of one frame, and the change rate ratio of the region. That is, in the case of cardiac function analysis, it becomes possible to estimate the cardiac volume from the movement of the heart, and in the case of blood flow analysis, it becomes possible to estimate the pulmonary blood flow rate from the cardiac output, and to estimate the estimated blood flow rate (rate) in the branching vessels depicted from the central blood flow rate (rate).

[0109] Also, as mentioned above, if the acquired database can be calculated in its entirety, a more accurate judgment can be made, but it may take time to perform computer analysis. Therefore, it is possible to extract only a certain number of images (phases) and perform calculations. For example, images are automatically acquired from the latter half of the acquired images, that is, images from the middle to the rear, not from the beginning (manual acquisition is also possible). This makes it easier to extract more stable images by cutting out the images taken in the first tense state when taking a patient's photograph. Also, instead of calculating the acquired images (for example, 300 images) as they are, it is possible to select a turning point of the "cardiac movement" based on the position of the heart measured initially, and then perform calculations. This makes it possible to make image marks that look like the heart is beating continuously when it is necessary to repeat video marks. This may be calculated using image marks. In addition, when identifying the area of ​​the heart (myocardium), even if the shape of only a part of the image is manually changed or only a part of the graph marks is manually changed, it is desirable to correct the graph using noise cut and least squares method.

[0110] As described above, according to this embodiment, it is possible to evaluate an image of a human body with an X-ray video device. If digital data can be acquired, calculations can be performed generally well with existing facility devices, and the introduction cost is low. For example, in an X-ray video device using a flat panel detector, it is possible to easily perform an examination of a subject. In cardiac function analysis, screening for myocardial infarction is possible. For example, in an X-ray video device using a flat panel detector, unnecessary examinations can be eliminated by executing the diagnostic support program according to this embodiment before performing CT. In addition, since the examination is simple, it is possible to find diseases with high urgency early and deal with them on a priority basis. In addition, in the current imaging method, there are some problems with other modalities such as CT and MRI, but if these problems can be solved, detailed diagnosis of each area will be possible.

[0111] It can also be applied to screening for narrowing of various blood vessels, for example, in the neck, and to evaluating and screening blood flow in large blood vessels. It can also be applied to grasping pre- and post-operative characteristics. Furthermore, by performing Fourier analysis on the cycle of heart movement and blood flow and removing the waveform of heart movement and blood flow from abdominal X-ray images, it becomes possible to observe abnormalities in the remaining biological movements, such as intestinal ileus.

[0112] If the image acquired initially is relatively high resolution, the number of pixels may be large, which may result in long calculation times. In that case, you can reduce the image to a certain number of pixels before performing calculations. For example, you can reduce calculation times by converting "4000 x 4000" pixels into "1028 x 1028" pixels before performing calculations.

[0113] In addition, in the past, the contrast width of the target image to be judged was adjusted manually. Or, a method was adopted in which the target image to be judged was relatively depicted based on a certain criterion. However, this was not performed strictly by recognizing the frame of the analysis target (for example, the lung field). By detecting the heart area and filtering the bone density (cutting out a certain range of low transparency) in the present invention, the transparency width inside the remaining area is strictly limited to the width for recognizing the heart area. This allows the transparency to be adjusted more strictly when detecting the heart area, so that the person making the judgment, such as a doctor or engineer, can easily judge. Also, the transparency can be adjusted more strictly to be just right for evaluating coloring.

[0114] In addition, in XP and CT, the X-ray penetration is changed according to the state of the body in order to reduce radiation exposure, and the penetration may change depending on the movement of the lungs during imaging. In addition, MRI also produces images with a certain level of non-uniform signals due to changes in the magnetic field in a specific direction. In response to these, the calculation to return the penetration that has changed from the penetration of the surrounding "background" to a certain form can be adjusted to the penetration of a specific organ, or the non-uniformity of the overall change in the magnetic field can be uniformly corrected to a certain level to correct the signal value, allowing for more precise measurement of the change in "density / intensity" of a specific organ. In addition, by changing the penetration based on the characteristics of each organ rather than the numerical change in the imaging conditions, and more precisely correcting the change in "density / intensity" of a specific organ, it becomes possible to calculate the amount of change more accurately.

[0115] In the present invention, the average value and changes of intensity may be calculated, but the data (intensity) obtained from X-rays, CT scans, etc. may have been subjected to "gamma correction", and the density may not be reflected correctly. This gamma correction is a process for correcting the saturation and brightness of an image displayed on a display. Normally, a computer displays an image on a display according to an input signal, but the displayed brightness and saturation may differ depending on the characteristics of the display. Therefore, gamma correction is used to correct these errors. Gamma correction adjusts the relative relationship of signals input and output to the display and the color data to display an image that is close to natural. However, since the image after gamma correction is no longer the original image, problems may occur when performing the image processing according to the present invention. Therefore, the image after gamma correction is subjected to "gamma inverse correction", that is, an inverse filter corresponding to the gamma correction value is applied to obtain the image before gamma correction. This makes it possible to obtain the image before gamma correction, and to appropriately perform the image processing according to the present invention.

[0116] In addition, in the present invention, images that have been subjected to other image processing, not limited to gamma correction, may be restored to their original state before being subjected to further processing. For example, the image processing performed on the image is inferred from the pixel value change in an area where density does not change during shooting, such as a space outside the human body, and a function that makes the pixel value change constant is applied to all pixels. This makes it possible to obtain an image close to the original image, and to appropriately perform the image processing according to the present invention.

[0117] In addition, in the present invention, it is possible to restore individual images from superimposed images. Conventionally, "X-ray difference image technology" has been known. In this technology, "past and present X-ray images" of the same patient taken during medical examinations are superimposed, and the parts that have changed from the past to the present, that is, the parts that are assumed to be abnormal, are emphasized. This makes it possible to detect cancer at an early stage, for example. As a method of superimposition, when multiple images are taken in the order of "first, second, third, fourth, ...", for example, "first, second, third" is the "first superimposed image," "second, third, fourth" is the "second superimposed image," and "third, fourth, fifth" is the "third superimposed image." In such superimposed images, the pixel value is high in the parts where multiple images overlap, and the pixel value is low in the parts where they do not overlap. In such superimposed images, the outline of the organs may be blurred, so it is desirable to restore them to the original images. According to the present invention, since the contours of the organs can be specified, it becomes possible to restore the original image from the superimposed image.

[0118] In addition, the present invention can visualize the difference in frequency between regions in an organ. That is, in an organ that moves periodically, the change value for each region is Fourier transformed, and weighting is performed, such as color-coding, based on the composition ratio of frequency components, to show the characteristics of each region. For example, it is possible to identify the frequency components that are peaks in each region and color-code each region. It is also possible to identify the proportion of each frequency component in a specific frequency band in each region and color-code each region according to that proportion. Furthermore, in a specific region, when the reference frequency composition ratio is, for example, "10 Hz 50% and 20 Hz 50%," it is also possible to visualize the deviation rate from the reference frequency composition ratio. As a result, for example, for the heart, by displaying the frequency spectrum distribution in different colors, it is possible to grasp at a glance whether the heart is moving correctly or incorrectly. [Explanation of symbols]

[0119] 1 Basic Module 3. Cardiac Function Analysis Department 5 Blood flow analysis section 7 Other Blood Flow Analysis Section 9 Fourier Analysis Section 10 Waveform analysis section 11 Visualization and Quantification Department 13 Input Interface 15 Database 17 Output Interface 19 Display

Claims

1. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of calculating a part of a waveform indicating a periodic movement specific to an organ between the frame images, or a frequency specific to an organ between the frame images, based on pixel values ​​between the frame images; A process of performing a Fourier transform on the image change between each of the frame images; A process of extracting a part of a waveform showing a periodic movement specific to the organ or a spectrum within a certain band including a spectrum corresponding to a frequency specific to the organ from the spectrum obtained after the Fourier transform; performing an inverse Fourier transform on the spectrum extracted from the certain band; and outputting each image after the inverse Fourier transform.

2. The method further includes a process of dividing an image of a human organ into a plurality of block regions and calculating a change in an image of the block region in each of the frame images; 2. The diagnostic support program according to claim 1, wherein the Fourier transform processing performs a Fourier transform on changes in the image of each of the block regions in each of the frame images.

3. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of calculating a part of a waveform indicating a periodic movement specific to an organ between the frame images, or a frequency specific to an organ between the frame images, based on pixel values ​​between the frame images; extracting, by a digital filter, a portion of a waveform showing a periodic movement specific to an organ in the image, or a pixel that changes in response to a frequency specific to an organ in the image; and outputting an image including the pixels extracted by the digital filter.

4. Dividing the image of the organ into a plurality of block regions and calculating changes in the image of the block regions in each of the frame images, The diagnostic support program according to claim 3, characterized in that the process of extracting, using a digital filter, a portion of a waveform indicating a periodic movement specific to an organ in the image, or a pixel that changes in response to a frequency specific to an organ in the image, extracts the portion using a digital filter for each block area.

5. A process of calculating a part of a waveform showing a periodic movement specific to an organ between the frame images, or a rate of change of a frequency specific to an organ between the frame images, based on pixel values ​​between the frame images; selecting a color corresponding to a portion of a waveform that indicates a periodic movement specific to said organ, or a rate of change of a frequency specific to said organ; 2. The diagnostic support program according to claim 1, further comprising a step of: applying the selected color to the rate of change of the pixel value and displaying the image on a display.

6. Dividing the image of the organ into a plurality of block regions and calculating a rate of change of the image of the block region in each of the frame images, The diagnostic support program according to claim 5, characterized in that the process of selecting a color corresponding to a portion of a waveform indicating a periodic movement specific to the organ, or a rate of change of a frequency specific to the organ, selects a color for each of the block regions.

7. The block region is formed using the Voronoi division method.

7. The diagnostic support program according to claim 2, 4 or 6.

8. A diagnostic support program for analyzing an image of a human body and displaying an analysis result, A process of acquiring a plurality of frame images; A process of calculating a part of a waveform indicating a periodic movement specific to an organ between the frame images, or a frequency specific to an organ between the frame images, based on pixel values ​​between the frame images; A process of defining an analysis range for all the acquired frame images; Dividing the analysis range into multiple regions; and classifying each of the divided regions based on a portion of a waveform that indicates periodic movement or an index of frequency.

9. Calculate the change in pixel value of each of the plurality of frame images while maintaining the absolute positional relationship of each pixel in the frame images. The diagnostic support program according to any one of claims 1 to 8.

10. The diagnostic support program according to claim 9, characterized in that a plurality of frame images are classified into a plurality of groups, and periodic changes characterizing the state of an organ are calculated while maintaining the absolute positional relationship of each pixel within the plurality of frame images belonging to each group.

11. A diagnostic support program as described in claim 2, claim 4 or claim 6, characterized in that the change in the image of the block region in each of the frame images is calculated while maintaining the relative positional relationship of the pixels representing the organ between each of the frame images, regardless of whether they are adjacent frames or not.

12. A diagnostic support program according to any one of claims 1 to 9, 10 and 11, further comprising a process for restoring a changed transmittance to a constant form from the transmittance of a specific region in a frame image captured by X-rays.

13. The diagnostic support program according to any one of claims 1 to 9, 10 and 11, further comprising a process of correcting signal values ​​in areas of a frame image taken by MRI where the MRI magnetic field is inhomogeneous, and converting the image into an image that would be obtained if the magnetic field were homogeneous.

14. A diagnostic support program as described in claim 2, claim 4 or claim 6, characterized in that a rate of change is calculated from the manner of change of the entire organ between each frame image, regardless of whether they are adjacent frames, and the change in the image of the block region is calculated based on the calculated rate of change.

15. 15. The diagnostic support program according to claim 14, wherein the rate of change varies depending on the position of the block region in the organ, or is constant throughout the organ.

16. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of calculating a periodic change that characterizes a state of an organ between the frame images based on pixel values ​​between the frame images; A process of performing a Fourier transform on the image change between each of the frame images; A process of extracting a spectrum within a certain band including a spectrum corresponding to a frequency of the movement of an organ from the spectrum obtained after the Fourier transform; performing an inverse Fourier transform on the spectrum extracted from the certain band; A process of outputting each image after the inverse Fourier transform; A process of setting a maximum outer edge when the organ has a maximum size; A process of setting a minimum outer edge when the size of the organ is minimum; A process of calculating coefficients of outer edges of organs of other sizes in each image using the maximum outer edge and the minimum outer edge; displaying on a graph a waveform corresponding to a coefficient of an outer edge of an organ in each of the images and control points of the waveform; and changing the positions of the control points to change the coefficients of each image.

17. 17. The diagnostic support program according to claim 16, wherein an average pixel value of the image of the organ is displayed on the graph.

18. 18. The diagnostic support program according to claim 16, wherein the image of the organ is displayed in juxtaposition with the graph.

19. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of dividing an image of a human organ in each of the frame images into a plurality of block regions; A process of calculating changes in an image of a block region in each of the frame images; A process of Fourier transforming the change value of each block region; and color-coding each region based on the component ratio of frequency components.

20. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of extracting pixels having different coordinates from each pixel in a specific frame image in subsequent frame images, and calculating a part of a waveform showing a periodic movement specific to an organ, or a frequency specific to an organ between each of the frame images; A process of performing a Fourier transform on the image change between each of the frame images; A process of extracting a part of a waveform showing a periodic movement specific to an organ or a spectrum within a certain band including a spectrum corresponding to a frequency specific to the organ from the spectrum obtained after the Fourier transform; performing an inverse Fourier transform on the spectrum extracted from the certain band; and outputting each image after the inverse Fourier transform.

21. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of extracting pixels having different coordinates from each pixel in a specific frame image in subsequent frame images, and calculating a part of a waveform showing a periodic movement specific to an organ, or a frequency specific to an organ between each of the frame images; extracting, by a digital filter, a portion of a waveform showing a periodic movement specific to an organ in the image, or a pixel that changes in response to a frequency specific to an organ in the image; and outputting an image including the pixels extracted by the digital filter.

22. A diagnostic support program for analyzing an image of a human organ and displaying an analysis result, A process of acquiring a plurality of frame images; A process of extracting pixels having different coordinates from each pixel in a specific frame image in subsequent frame images, and calculating a part of a waveform showing a periodic movement specific to an organ, or a frequency specific to an organ between each of the frame images; A process of defining an analysis range for all of the acquired frame images based on a part of a waveform indicating a periodic movement specific to an organ between the frame images, or a frequency specific to an organ between the frame images; A process of calculating a rate of change between each of the frame images in the analysis range; a process of selecting a color corresponding to a rate of change between the frame images; and displaying the image on a display with the selected color.

23. A process of calculating a difference or ratio between any one of the frame images and other frame images selected at any interval; A process of visualizing an analysis range set for each of the frame images using the calculated difference or ratio; 9. The diagnostic support program according to claim 8, further comprising:

24. A process of calculating pixel values ​​in each of the frame images as relative values ​​or logarithmic values; A process of calculating a ratio between any one of the frame images displayed as the relative value or logarithm value and another frame image displayed as the relative value or logarithm value and selected at an arbitrary interval; and a process of visualizing the calculated ratio, 9. The diagnostic support program according to claim 8, further comprising: calculating pixel values ​​of a lung field area in one or more frame images based on a change in density of the lung field or a positional relationship of blood vessels.

25. A process of calculating a difference or ratio between any one of the frame images and other frame images selected at any interval; A process of visualizing an analysis range set for each of the frame images using the calculated difference or ratio; Equipped with 9. The diagnostic support program according to claim 8, further comprising: calculating pixel values ​​of frame images from which artifacts have been removed.

26. A process of calculating pixel values ​​in each of the frame images as relative values ​​or logarithmic values; A process of calculating a ratio between any one of the frame images displayed as the relative value or logarithm value and another frame image displayed as the relative value or logarithm value and selected at an arbitrary interval; A process of visualizing the calculated ratio; Equipped with 9. The diagnostic support program according to claim 8, further comprising: calculating pixel values ​​of frame images from which artifacts have been removed.

Images