JP6013042B2 - Image processing program, recording medium, image processing apparatus, and image processing method - Google Patents

Description

  The present invention relates to a technique for processing an image obtained by imaging a distribution state of a radiopharmaceutical in a body of a subject.

  Conventionally, when making a diagnosis based on an image obtained by imaging the distribution state of a radiopharmaceutical in a subject's body, for example, a bone scintigraphy image (hereinafter referred to as a bone scintigram), a doctor or other interpreter visually confirms the image Then, the location where the radiopharmaceutical is abnormally accumulated is specified, and the diagnosis is performed based on this. In this case, whether or not a correct diagnosis can be made depends on the experience of the interpreter.

  Thus, a technique is known in which the accuracy of diagnosis is improved by performing subtraction between digital medical images that are temporally continuous to emphasize temporal changes (for example, Patent Document 1). In addition, a technique for generating a difference image between an image captured in the past (past image) and an image captured this time (current image) in a bone scintigram of the same subject to assist an image reader is known (for example, a patent) Reference 2).

Japanese Patent No. 4130661 International Publication WO2007 / 062135 A2

A new computer-based decision-support system for the impression of bone scan by Sadik M.A. et al published in Nucleic Medicine Communication nr. 27: pp. 417-423. Active shape models-tear training and application, T.A. F. Cootes, C.I. J. et al. Taylor, D.D. H. Cooper and J.M. Graham presented in Computer Vision and Image Understanding, Vol. 61, no. 1, pp. 38-59, 1995. Application of the Active Shape Model in a commercial medical device for bone densitometry, H.C. H. Thodberg and A.M. Rosholm presented in the Processings of the 12th British Machine Vision Conference, 43-52, 2001. Average brain models: A convergence study, Guimond A. Meunier J.M. Tillion J. -P presented in Computer Vision and Image Understanding, 77 (2), pp. 192-210, 2000. Non-Rigid Registration using Morphons, A.R. Wrangsjo, J .; Petersson, H.C. Knutsson presented in Proceedings of the 14th Scandinavian conference on image analysis (SCIA'05), Joensuu June 2005. Morphons: Segmentation using Elastic Canvas and Paint on Priors, H.M. Knutsson, M.M. Andersson presented in ICIP 2005, Genova, Italy, September 2005.

  When performing a plurality of examinations over a long period of time on the same subject, imaging conditions such as the type of detector, the physical condition of the subject, and the dose of the radiopharmaceutical may differ depending on the examination. In this case, for example, an equivalent abnormality in a plurality of images may be represented by greatly different pixel values. Therefore, even if these multiple images are displayed side by side, it is not easy for an image interpreter to compare these images and make an appropriate determination.

  Therefore, an object of the present invention is to provide information to an image reader so that the images can be compared with high accuracy even when the imaging conditions of a plurality of images obtained by imaging the distribution state of the radiopharmaceutical in the subject's body are different. That is.

  An image processing program according to one embodiment of the present invention is an image processing program for a computer that stores an image obtained by imaging a distribution state of a radiopharmaceutical in a body of a subject, and is evaluated based on a pixel value in the image. A calculation step for calculating a value; a determination step for determining whether or not the evaluation value is outside a specific range; and if the evaluation value is outside the specific range, a pixel value in the image is set as the evaluation value. And making the computer execute a change step that changes based on the change step.

  In a preferred embodiment, when the evaluation value is outside the specific range, the changing step changes the pixel value of the image so that the evaluation value of the changed image becomes a specific value within the specific range. May be.

  In a preferred embodiment, the specific range and the specific value may be determined based on a plurality of evaluation values respectively calculated based on a plurality of images.

  In a preferred embodiment, the calculating step may calculate the evaluation value based on a sum of pixel values in the image.

  In a preferred embodiment, the calculating step may calculate the evaluation value based on a sum of pixel values of a specific region in the image.

  In a preferred embodiment, the image is a bone scintigram and may include an anterior and posterior image of the subject.

  In a preferred embodiment, the calculating step calculates the evaluation value based on a pixel value in the front image and a pixel value in the rear image, and the changing step includes determining that the evaluation value is outside the specific range. In this case, the pixel value in the front image and the pixel value in the rear image may be changed based on the evaluation value.

  In a preferred embodiment, a normalization step of normalizing a plurality of images including the image, and generating a display image by arranging the normalized plurality of images corresponding to the time when the images were captured. A display control step for displaying the display image on a display device may be executed by a computer.

  In a preferred embodiment, the computer stores size information related to a size of a field of view of the plurality of images, and the display control step is configured to determine the normalized plurality of normalized images in the display image based on the size information. The field of view of the image may be matched.

  In a preferred embodiment, the display control step uses a reference value determined based on a maximum pixel value in an image obtained by normalizing an image obtained by imaging a distribution state of a radiopharmaceutical in another subject's body. The pixel values not more than the reference value in the normalized images may be converted to gray scale.

  In a preferred embodiment, the display control step arranges front images in the normalized image pairs in the first region in the display image in the order of the plurality of times, and the display image. In the second region, rear images of the normalized image pairs may be arranged in the order of the times.

  In a preferred embodiment, the display control step may arrange the plurality of normalized image pairs in the display image in the order of the plurality of times.

  In a preferred embodiment, the normalizing step detects a normal region that is a healthy region from each of the plurality of images, and calculates a pixel value of the image including the normal region based on a pixel value of the normal region. You may normalize.

  In a preferred embodiment, the normalizing step may identify a skeletal site in each of the plurality of images and detect the normal region from the skeletal site.

  In a preferred embodiment, the normalizing step detects a plurality of regions from each of the plurality of images, and selects an abnormal region that is an unhealthy region and the normal region from the plurality of detected regions. It may be detected.

  In a preferred embodiment, the normalizing step calculates an average value of pixel values of the normal area for each of the plurality of images, and divides a pixel value of an image including the normal area by the average value. The pixel value of the image including the normal area may be normalized by

  In a preferred embodiment, the normalizing step detects a specific region in which a pixel value satisfies a predetermined condition from the skeletal region, and detects a region excluding the specific region from the skeleton region as the normal region. May be.

  In a preferred embodiment, the normalizing step may detect, as the specific region, a region where a pixel value exceeds a predetermined threshold value from the skeleton region.

It is a block diagram which shows the structure of the image processing apparatus 1 which concerns on this embodiment. 3 is a flowchart showing an operation of the image processing apparatus 1. 4 is a flowchart showing the operation of the correction unit 20. 3 is a block diagram illustrating a configuration of a normalization unit 30. FIG. 3 is a flowchart showing a bone scintigram dividing method by a normalization unit 30. It is a figure which shows an example of a bone scintigram and a skeleton part. It is an example of a health reference image set 4 is a flowchart illustrating a normalization method performed by a normalization unit 30. It is a figure which shows an example of the normalized bone scintigram set. It is a histogram which shows distribution of the count value of the bone scintigram before normalization. It is a histogram which shows distribution of the normalization count value of the bone scintigram after normalization. It is a figure which shows an example of the relationship between the order | rank of the average pixel value of a high intensity | strength area | region in a bone scintigram, and the average pixel value of a high intensity | strength area | region. It is a figure which shows a scale range. It is a screen which shows a 1st display mode. It is a screen which shows a 2nd display mode.

  Hereinafter, an image processing apparatus according to an embodiment of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating a configuration of an image processing apparatus 1 according to the present embodiment. As shown in FIG. 1, the image processing apparatus 1 includes an image processing apparatus main body 10, an input device 4 such as a keyboard, a touch panel, and a pointing device, and a display device 5 such as a liquid crystal display. The image processing apparatus main body 10 includes a correction unit 20, a normalization unit 30, a display control unit 60, and a storage unit 70. The display control unit 60 includes an adjustment unit 40, a conversion unit 50, and a generation unit 80.

  The image processing apparatus main body 10 is configured by, for example, a general-purpose computer system, and individual components or functions in the image processing apparatus main body 10 described below are realized by, for example, executing a computer program. The computer system has a memory and a microprocessor. The storage unit 70 may be realized by a memory. The computer program can be stored and distributed in a computer-readable recording medium.

  The storage unit 70 includes a captured image storage unit 15, a corrected image storage unit 25, a normalized image storage unit 35, a feature data storage unit 36, and an adjustment image storage unit 45. The captured image storage unit 15 stores a captured image that is a captured bone scintigram. The bone scintigram is an image obtained by imaging the distribution state of the radiopharmaceutical in the body of the subject. The captured image set is a pair of a captured image of a front image and a captured image of a rear image captured by a certain examination of a certain patient. In this embodiment, the value (pixel value) of each pixel in the bone scintigram is a count indicating the radiation intensity from a radioactive substance (radioisotope: radioisotope) injected into the human body before taking an image. Value. In addition, the captured image storage unit 15 stores imaging condition data including the inspection date and time, information indicating the front or rear surface, a reference visual field, and the like in association with the captured image.

  FIG. 2 is a flowchart showing the operation of the image processing apparatus 1. This flow may be executed every time a captured image set is stored in the captured image storage unit 15 by an examination, or a plurality of captured image sets of a certain patient are stored in the captured image storage unit 15. It may be performed for examination or analysis.

  The correction unit 20 corrects the captured image stored in the captured image storage unit 15 in accordance with a predetermined standard, and stores the corrected image as a result in the corrected image storage unit 25 (S20).

  Thereafter, the normalization unit 30 detects features such as a skeletal region and a hot spot from the corrected image stored in the corrected image storage unit 25, and associates the resulting feature data with the corrected image to the feature data storage unit 36. Save to Further, the normalizing unit 30 normalizes the corrected image based on the feature data, and stores the normalized image as a result in the normalized image storage unit 35 (S30).

  Thereafter, the display control unit 60 generates a display screen by adjusting and arranging the normalized images stored in the normalized image storage unit 35, and causes the display device 5 to display the display screen (S60). End the flow.

  In the display control unit 60, the adjustment unit 40 adjusts the size of the normalized image stored in the normalized image storage unit 35 and stores it in the adjusted image storage unit 45 as a result. The conversion unit 50 converts the pixel value of the adjusted image stored in the adjusted image storage unit 45 into a gray scale density. The generation unit 80 generates a display screen by arranging the adjustment images in time series and causes the display device 5 to display the display screen.

  Note that the generation unit 80 may arrange the feature data stored in the feature data storage unit 36 in the display screen in association with the adjustment image.

  According to the image processing apparatus 1, it is possible to reduce the influence of differences in imaging conditions in a plurality of captured bone scintigrams. In addition, by displaying a plurality of bone scintigrams on the display device 5 according to a time series, it becomes easy for an image interpreter to determine temporal changes of the plurality of bone scintigrams.

  Details of the correction unit 20 will be described below.

  FIG. 3 is a flowchart showing the operation of the correction unit 20. In S20 described above, the correction unit 20 executes this flow.

  The correction unit 20 reads the captured image set from the captured image storage unit 15 and determines whether or not the read captured image set satisfies a predetermined standard (S2020).

  For example, the correction unit 20 calculates an evaluation value from the count value of pixels in a specific area in the captured image set, and if the evaluation value is within a standard range, the captured image set satisfies a predetermined standard. judge. The evaluation value is calculated based on, for example, the sum of the count values of all pixels in the previous image and the subsequent image. A more preferable evaluation value is represented by (sum of count values of all pixels of the previous image + sum of count values of all pixels of the subsequent image) / 2. The standard range is determined based on, for example, a captured image database that stores a plurality of captured image sets captured under standard imaging conditions. That is, if the evaluation value of the captured image set is within the standard range, the correcting unit 20 determines that the captured image set is captured under the standard imaging condition. The lower limit of the standard range is, for example, smaller than the minimum value of the evaluation value calculated from each captured image set in the captured image database, and the upper limit of the standard range is, for example, the maximum of the evaluation value calculated from each captured image set in the captured image database. Greater than value. In the standard range determined based on the actual captured image database, the lower limit is, for example, 1,200,000, and the upper limit is, for example, 2,200,000.

  When the captured image set satisfies the standard (S2020: Yes), the correcting unit 20 stores the captured image set in the corrected image storage unit 25 as a corrected image set without correcting (S2030), and ends this flow.

  When the captured image set does not satisfy the standard (S2020: No), the correction unit 20 calculates a scaling factor based on the evaluation value, and generates a corrected image set by multiplying each pixel of the captured image set by the scaling factor. And it preserve | saves to the correction image memory | storage part 25 (S2040), and complete | finishes this flow. The scaling factor is expressed by, for example, (reference evaluation value / evaluation value). By multiplying each pixel of the captured image set determined not to satisfy the standard by the scaling factor, the evaluation value of the modified image set as a result becomes the reference evaluation value. The reference evaluation value is a value within the standard range. The reference evaluation value determined based on the actual captured image database is 1,800,000, for example.

  The correction unit 20 reads the imaging condition data corresponding to the corrected image from the captured image storage unit 15 and stores it in the corrected image storage unit 25 in association with the corrected image.

  Here, a modification of the correction unit 20 will be described.

  The correction unit 20 may determine the standard range and the reference evaluation value based on the statistic of the evaluation value calculated from each captured image set in the captured image database.

  In the first modification, the correction unit 20 calculates the average value and the standard deviation SD of the evaluation values calculated from each captured image set in the captured image database. Furthermore, the correction unit 20 sets the average value of the evaluation values as the reference evaluation value, sets (average value−predetermined deviation) as the lower limit of the standard range, and sets (average value + predetermined deviation) as the upper limit of the standard range. The predetermined deviation is determined based on SD such as SD or 2SD.

  In the second modification, the correcting unit 20 arranges the evaluation values calculated from the respective captured image sets in the captured image database in descending order to detect the median evaluation value. At this time, the correction unit 20 sets the median value of the evaluation values as the reference evaluation value, sets the evaluation value positioned lower than the median by a predetermined number as the lower limit of the standard range, and sets the evaluation value positioned higher than the median as the predetermined value. Is the upper limit of the standard range. The predetermined number is, for example, a value obtained by multiplying the number of captured image sets (number of cases) in the captured image database by a predetermined ratio. The predetermined ratio is, for example, 25%.

  In the third modification, the correction unit 20 sets the median evaluation value as the reference evaluation value, sets the evaluation value lower than the median by a predetermined deviation as the lower limit of the standard range, and standardizes the evaluation value higher than the median by the predetermined deviation. The upper limit of the range. The predetermined deviation is determined based on SD such as SD or 2SD.

  In the fourth and fifth modified examples, the evaluation value is calculated based on the sum of the count values of the pixels in the specific bone region in the previous image and the subsequent image. In this case, the correction unit 20 may detect a specific bone region from the captured image set, or may use a preset bone region. A more preferable evaluation value is represented by (sum of count values in a specific bone region of the previous image + sum of count values in a specific bone region of the subsequent image) / 2.

  In the fourth modification, the correction unit 20 calculates the average value, the maximum value, and the minimum value of the evaluation values calculated from each captured image set in the captured image database. The correction unit 20 sets the average value of the evaluation values as the reference evaluation value, sets the minimum value of the evaluation values as the lower limit of the standard range, and sets the maximum value of the evaluation values as the upper limit of the standard range.

  In the fifth modification, the correction unit 20 calculates the average value and the standard deviation SD of the evaluation values calculated from each captured image set in the captured image database. At this time, the correction unit 20 sets the average value of the evaluation values as the reference evaluation value, sets the value obtained by subtracting the predetermined deviation from the average value as the lower limit of the standard range, and sets the value obtained by adding the predetermined deviation to the average value as the upper limit of the standard range. To do. The predetermined deviation is determined based on SD such as SD or 2SD.

  The above is the operation of the correction unit 20.

  As described above, the correction unit 20 can correct the captured image set whose count value is determined to be out of the standard so as to satisfy the standard. In other words, the count value of the captured image set that is not captured under the standard imaging condition can be corrected within the range of the count value of the captured image set that is captured under the standard imaging condition. Thereby, the accuracy of subsequent processing can be improved.

  Hereinafter, the normalization unit 30 will be described.

  The normalization unit 30 reads the corrected image set from the corrected image storage unit 25. In the following description, the corrected image may be called a bone scintigram or a patient image.

  FIG. 4 is a block diagram showing a configuration of the normalization unit 30. As shown in FIG. The normalization unit 30 includes an input image memory 105, a shape identification unit 110, an annotated image memory 115, a hot spot detection unit 120, a first hot spot memory 125, a hot spot feature extraction unit 130, a second It has a hot spot memory 135, a first ANN unit 140, a third hot spot memory 145, a patient feature extraction unit 150, a patient feature memory 155, and a second ANN unit 160. Here, ANN is an artificial neural network.

  The input image memory 105 stores a bone scintigram set. The bone scintigram set obtained by one diagnosis has a front image captured from the front of the patient and a rear image captured from the back of the patient.

  The shape identifying unit 110 is connected to the input image memory 105 and identifies the anatomical structure of the skeleton included in the bone scintigram set. The shape identifying unit 110 has a predefined skeleton model. The skeletal model has at least one anatomical site (segment). Each site represents a general skeletal anatomical part. The skeletal model is adjusted to fit the skeleton of the bone scintigram set.

  The annotated image memory 115 is connected to the shape identifying unit 110 and stores an annotated image set. The annotated image set is based on information regarding a plurality of anatomical structures corresponding to a plurality of skeletal parts having different bone scintigram sets from the shape identification unit 110.

  The hot spot detection unit 120 is connected to the annotated image memory 115 and detects a high intensity region of the annotated image set. One form of hot spot detector 120 has a threshold scan that scans the bone scintigram set and identifies pixels that exceed a certain threshold. The preferred hot spot detector 120 has different thresholds for different anatomical sites defined by the shape identifier 110. In this case, the hot spot feature extraction unit 130 extracts at least one hot spot feature for each hot spot, and determines the shape and position of each hot spot.

  Another form of hot spot detection unit 120 may include an image normalization filtering unit that scans the bone scintigram set and identifies pixels that exceed a certain threshold. In this case, the hot spot feature extraction unit 130 extracts at least one hot spot feature for each hot spot, and determines the shape, texture, and geometric shape of each hot spot. Details of each feature within the preferred set of hot spot features will be described below as a specific example of the input of the first ANN unit 140.

  Regarding the detected high intensity region, so-called “hot spot”, information generated as described above is stored in the first hot spot memory 125.

  The hot spot feature extraction unit 130 is connected to the first hot spot memory 125 and extracts a set of hot spot features of each hot spot detected by the hot spot detection unit 120. The extracted hot spot feature is stored in the second hot spot memory 135.

  The first ANN unit 140 is connected to the second hot spot memory 135, and calculates the likelihood (hot spot metastasis possibility) that each hot spot of the hot spot set is metastasis (cancer metastasis, bone metastasis). The first ANN unit 140 is supplied with the features of each hot spot of the hot spot set generated by the hot spot feature extracting unit 130. The likelihood calculation is based on the hot spot feature set extracted by the hot spot feature extraction unit 130. The result of the likelihood calculation is stored in the third hot spot memory 145.

  Preferably, the ANN learned for each anatomical part is arranged in the first ANN unit 140. Each hot spot within a site is processed sequentially by an ANN arranged to process hot spots from that site.

  The patient feature extraction unit 150 is connected to the second hot spot memory 135 and the third hot spot memory 145, and the number of hot spots detected by the hot spot detection unit 120 and stored in the second hot spot memory 135. The patient feature set is extracted based on the likelihood output value from the first ANN unit 140 stored in the third hot spot memory 145. The patient feature extraction unit 150 performs calculation using both the data from the hot spot feature extraction unit 130 and the data output from the first ANN unit 140. The extracted patient feature is stored in the patient feature memory 155. A suitable extracted feature will be described later as a specific example of the input of the second ANN unit 160.

  The second ANN unit 160 is connected to the patient feature memory 155, and the patient has at least one cancer metastasis based on the patient feature set extracted by the patient feature extraction unit 150 and stored in the patient feature memory 155. Calculate the metastatic potential (patient metastasis potential). The normalization unit 30 may optionally include a threshold determination unit 165. One form of the threshold determination unit 165 has a value corresponding to “yes, the patient has at least one cancer metastasis” when the likelihood output from the second ANN unit 160 exceeds a predefined threshold. "Yes, the patient does not have cancer metastasis" when the likelihood output from the second ANN unit 160 does not exceed a predefined threshold value, and "yes" Or “no” determination. Another optional form of threshold determination unit 165 classifies the output into one of four diagnoses. The four diagnoses are “definitely normal”, “probably normal”, “probably metastasized”, and “definitely metastatic”.

  Tests performed using these forms indicate that the normalization unit 30 based on any of the above forms worked well. In one of the above forms, the sensitivity was measured up to 90% and the specificity was measured up to 90%. This test method is the same as the test method described in Non-Patent Document 1.

In the optional form described above, performance was measured at three settings corresponding to the threshold used to classify the output value into one of four diagnoses. The sensitivity and specificity in these settings are as follows.
Certainly normal / probably normal: sensitivity 95.1%, specificity 70.0%
Probably normal / probably metastatic: sensitivity 90.2%, specificity 87.3%
Probably metastases / definitely metastases: sensitivity 88.0%, specificity 90.1%

  Hereinafter, a bone scintigram dividing method (segmentation) by the normalization unit 30 will be described.

  FIG. 5 is a flowchart showing a bone scintigram dividing method by the normalization unit 30. In S30 described above, the normalization unit 30 executes this flow.

  The bone scintigram division method uses, for example, an ASM (active shape model) approach, and the entire outline of the front and back images of the skeleton excluding the arms (upper limbs) and the lower parts (ends) of the legs (lower limbs). Includes performing extraction. Omitting these parts of the skeleton is not a problem because the lower arms and legs are very rare places for metastases and they are sometimes not acquired in the bone scanning routine. For illustration purposes, ASM is defined as a statistical method for finding objects in an image. The method is based on a statistical model based on a learning image set. In each learning image, the shape of the object is defined by target points that are determined manually by a human operator during the preparation or learning phase. A point distribution model is then calculated that is used to describe the general shape for the object. The general shape can be used to search for other images for a new example of an object type, such as a skeleton, as in the present embodiment. A method is provided for learning ASM that describes the anatomy of the human skeleton. The model includes the steps described below.

The first step, for example, divides the skeleton into 8 different learning sets (S205). The learning set is selected to correspond to an anatomical site that is particularly suitable for achieving consistent segmentation. Eight different learning sets are shown below.
1) The first learning set for the frontal image of the head and spine.
2) A second learning set for the frontal image of the ribs.
3) A third learning set for the frontal image of the arm.
4) A fourth learning set for the front image of the lower body.
5) A fifth learning set for the posterior image of the head and spine.
6) A sixth learning set for the posterior image of the ribs.
7) A seventh learning set related to the rear image of the arm.
8) The 8th learning set regarding the rear image of the lower body.

  Each learning set provides many case images (S210). Each image is created using a set of target points. Each target point is associated with a coordinate pair that represents a particular anatomical or biological point of the image. A coordinate pair is determined by manually identifying the corresponding anatomical / biological point (S215). In the front view, the following easily identifiable anatomical target points are used:

  Prior to capturing the learning set statistics, each set of target points is aligned to a different common coordinate system for each of the eight learning sets. This is realized by scaling, rotation, and interpretation of learning shapes that make the learning shapes as close as possible to each other, as described in Non-Patent Document 2. By examining the statistics of the learning set, a two-dimensional statistical point distribution model including the shape change observed in the learning set is obtained (S220). The statistical modeling of the change of the target point (shape) across the plurality of skeletons is performed as described in Non-Patent Document 2 and Non-Patent Document 3.

  The statistical model resulting from the shape change can be applied to the patient image to segment the skeleton. Starting with an average shape, new shapes within the range of possible changes in the shape model can be generated similar to the shape of the learning set so that the generated skeleton resembles the structure present in the patient image. . The frontal body segment divided using this method is, for example, skull, face neck, spine, upper sternum, lower sternum, right arm, left arm, right rib, left rib, right shoulder, left shoulder, pelvis, bladder, right femur, And the left femur. The posterior segment includes, for example, the skull, neck, upper spine, lower spine, spine, right arm, left arm, right rib, left rib, right shoulder blade, left shoulder blade, hip joint, lower pelvis, bladder, right femur, and left femur It may be.

  The first step in the search process may be to find a starting position for the average shape of the front image. For example, the highest point of the head may be selected. This is because it proves to be a robust starting position in the test and can be easily found by examining the intensity above a certain threshold in each horizontal line within the top of the image.

  Next, the search for the case of the skeleton shape model that matches the skeleton in the patient image is executed according to the algorithm described in Non-Patent Document 2 and Non-Patent Document 3.

  Through the above operation, the normalization unit 30 obtains feature data including the region of the skeletal site identified from the bone scintigram set, the region of the detected hot spot, the possibility of metastasis of the hot spot, the possibility of metastasis of the patient, etc. The feature data is stored in the feature data storage unit 36.

  Hereinafter, another bone scintigram division method by the normalization unit 30 will be described.

  Another bone scintigram segmentation method is provided. The goal of another bone scintigram segmentation method is to recognize and extract contours from a plurality of different anatomical parts of the skeleton in the bone scintigram 300, similar to the bone scintigram segmentation method described above. FIG. 6 is a diagram showing an example of a bone scintigram and a skeleton part. As shown in this figure, the skeletal site is defined by overlaying a contour 320 on the patient image 310. The division method described here is a division represented by registration.

  The image registration method converts an image into a coordinate system of another image. Assume that these images represent entities of the same object class, here a skeleton. The transformed image is represented by the source image, while the untransformed image is represented by the target image. If the same image coordinates match the same geometric / anatomical position in the objects contained in the source and target images, it can be said that the coordinate systems of the source and target images match. The division by registration is to use a manually defined division for the source image and to register the source image to the target image when no division is defined. The source image segment is thereby converted to the target image, thus producing a target image segment.

  The segmentation of the source image in this form defines the reference healthy patient's anatomy and is manually drawn by the laboratory specialist as a set of polygons. FIG. 7 is an example of a health reference image set. The health reference image set is called “Atlas”. Here, the health reference image set includes a health reference image 400a and a health reference image 400b. The health reference image 400a shows a front image or a front image of the patient, while the health reference image 400b shows a back image or a rear image of the patient. Referring to the labels in the health reference image 400a and health reference image 400b, respectively, these regions were labeled with the front and rear skulls 401, 402, (2, 2) labeled (1,1). Labeled with anterior and posterior cervical vertebrae 403, 404, (3, 3) and anterior and posterior thoracic vertebrae 405, 406, (14) and anterior sternum 407, (4, 4). Anterior and posterior pelvis labeled with anterior and posterior lumbar vertebrae 409, 408, (11, 5) and anterior and posterior sacrum 411, 410, (15, 14), (5, 6, 7,6) labeled anterior and posterior left and right shoulder blades, (17,16) labeled anterior and posterior left and right clavicles, anterior labeled (7,8,9,8) Part and rear Left and right humerus, (9,10,11,10) labeled anterior and posterior left and right ribs and (12,13,12,13) labeled anterior and posterior left and right femurs 413 415, 412, and 414 are defined.

  The health reference image set is always used as a source image by the normalization unit 30, while the patient image to be examined serves as a target image. The result is a segmentation of the target image into skeletal sites as depicted in the bone scintigram 300. Lower arms and lower limbs are not considered for analysis.

  The health reference image set used as the source image consists of 10 real cases of healthy patients with typical image quality and normal appearance and anatomy. An algorithm is used to generate an anterior and posterior image of a fictitious normal healthy patient having an average intensity and anatomy calculated from a group of case images. The normalization unit 30 performs this task as described in Non-Patent Document 4. The health reference image set shows the result when the resulting anatomy does indeed appear to have a normal health appearance. This anatomy is the result of averaging the anatomy of several patients, indicating high lateral symmetry.

  This registration method is an improvement of the Morphoon (form unit) registration method shown in Non-Patent Document 5 and Non-Patent Document 6. This method is improved to increase robustness for the purpose of skeletal image segmentation where both front and back images are given. Details of this improvement are shown below.

  The Morpho registration method improvement included in this embodiment consists of a system for using multiple images of the same object to determine a single image transformation. In particular, we use the front and back skeleton images simultaneously. The goal of this improvement is to increase the robustness of the method. For a description of this improvement, the necessary parts of the original Morpho registration method will be described first, followed by a description of this improvement.

  The following description of the so-called mutated vector field generation used within the Morphoon registration method introduces a notation and strives to make this improvement easier to see. More complete treatment is described in Non-Patent Document 5 and Non-Patent Document 6.

  The Morphoon registration method continues to iterate. Each iteration brings the source image closer to matching the target image. This corresponds to a small displacement of each source image element (pixel or voxel). All such sets of displacements during the iteration are collected in a vector field of the same size as the source image where each vector indicates the displacement of the corresponding image element. This vector field is determined using four complex filters. Each filter captures lines and edges in the image in a certain direction. The directions corresponding to the four filters are vertical, horizontal, diagonal from upper left to lower right, and diagonal from upper right to lower left. Filtering the image with one of these filters produces a complex response that can be divided into phase and magnitude. Due to the Fourier shift theorem, the phase difference at a particular point between the filtered source and target images is proportional to the spatial shift required to match the object to that point in the direction of the filter. If the phase and magnitude at each image point is calculated for all directions of the four filters, the displacement vector can be found by solving the least squares problem at each point. This magnitude can be used to derive a measure of certainty for each displacement estimate. Certainty can be incorporated into the least squares problem as a set of weights. The resulting weighted least squares problem is

  Here, v is the obtained 2 × 1 displacement vector, ni is the direction of the i th filter, vi is the phase difference corresponding to the i th filter, and wi is the i th filter. It is a certainty measure obtained from the size.

  This method improvement consists of using multiple images for the estimation of one vector field of displacement. Each image is filtered separately as described above to obtain four complex response results for each image. The weighted least squares problem is extended to include the entire image giving

  Here, k is the number of images (2 in the case of a bone scintigram). The effect of this is that the problem is better defined by multiplying the number of data points by the number of images in the estimate of the two-dimensional displacement v. This improvement is further explained by a certainty measure. Using a single image as input, the resulting displacement vector field portion corresponding to a low certainty measure is not well defined. Given multiple images, at least one image is likely to be able to provide sufficient certainty for all relevant sites.

  Hereinafter, the normalization method of the bone scintigram by the normalization part 30 is demonstrated.

  As described above, the hot spot detection unit 120 uses information from the shape identification unit 110. The hot spot detection unit 120 has two purposes. Its primary purpose is to separate hot spots in the front and back patient images. A hot spot is a high-intensity separated image site and may be an indicator of metastatic disease when in the skeleton. Its second purpose is to adjust the intensity of the image to a predefined reference level. Such intensity adjustment represents normalization of the image. Here, the algorithm will be described. This algorithm simultaneously isolates hot spots and estimates normalization factors. This algorithm is also executed separately for the front and rear images. In the following, the need for proper normalization will be briefly described first, followed by a description of this algorithm.

  Multiple bone scintigrams vary greatly in intensity levels depending on patient, study, and hardware configuration. This difference is presumed to be increasing and zero intensity is presumed to be a common reference level for all images. Thus, normalizing the source image with respect to the target image is to find a scalar factor that makes the intensity of the source image equal to the target image. Here, if the average intensity of the healthy part of the skeleton in the source image is equal to the corresponding part in the target image, the intensity of the two skeleton images is defined as equal. FIG. 8 is a flowchart showing a normalization method performed by the normalization unit 30. The normalization method shown in this flowchart has the following steps.

1. Identification of image elements corresponding to the skeleton (S510).
2. Identification of hot spots included in the image (S520).
3. The hot spot element is removed from the skeleton element (S530).
4). Calculation of the average intensity of the remaining (health) elements (S540).
5. Calculation of an appropriate normalization factor (S550).
6). Adjusting the source image intensity by multiplying by a normalization factor (S560).

  As described above, S510 is executed using the information on the image part belonging to the skeleton provided by the converted anatomical part obtained by the shape identifying unit 110. Polygonal parts are converted into binary image masks that define image elements belonging to the respective parts of the skeleton.

  In S520, the hot spot is divided using one image filtering operation and one threshold operation. Images are filtered using a DoG (difference-of-Gaussians) -BPF (band-pass filter) that emphasizes small areas of high intensity with respect to their surroundings. The filtered image is then thresholded at a certain level and the resulting binary image defines hot spot elements.

  In S530, among the elements calculated in S510, elements that match the hot spot element calculated in S520 are deleted. The remaining elements are assumed to correspond to healthy skeletal sites.

  In S540, the average strength of the health skeleton element is calculated. This average intensity is represented by A.

  In S550, an appropriate normalization factor is determined in relation to a predefined reference intensity level. Here, for example, this level can be set to 1000. The normalization factor B is calculated as (B = 1000 / A).

  In S560, the intensity of the source image is adjusted by multiplying B.

  Hot spot segmentation in S520 depends on the overall intensity level of the image, which is sequentially determined by the normalization factor calculated in S550. Nonetheless, the normalization factor calculated in S550 depends on the hot spot split from S520. Since the results of S520 and S550 are interdependent, S520 to S560 may be repeated, for example, until no further change in normalization factor occurs in S570. Extensive testing has shown that this process usually converges in 3 or 4 iterations.

  FIG. 9 is a diagram showing an example of a normalized bone scintigram set. The normalized image 600a shows a front image of the normalized bone scintigram set, and the normalized image 600b shows a rear image of the normalized bone scintigram set. The divided hot spots 620 are shown in the normalized image 600 a and the normalized image 600 b as dark spots that appear in the divided image 610. Therefore, normalizer 30 will classify this patient as having cancer metastasis.

  The normalization unit 30 stores the result of normalization of the corrected image by the above-described normalization method in the normalized image storage unit 35 as a normalized image.

  The second ANN unit 160 described above may be the same as the first ANN unit 140 or may be a part of the first ANN unit 140.

  The term “point” in the above description may be used to represent at least one pixel in the image.

  Hereinafter, a specific example of input to the first ANN unit 140 will be described.

  The first ANN unit 140 is provided with the following 27 features for measuring the size, shape, direction, localization, and intensity distribution of each hot spot.

-Skeletal lesions. A measure of the skeletal volume occupied by the extracted hot spot site based on the 2D hot spot region, the 2D region of the corresponding skeletal region, and a coefficient representing the volume fraction represented by the skeletal site for the entire skeleton. . Calculated as (hot spot area / local area * coefficient).
• Relative area. Hot spot area for the corresponding skeletal site. Scale independent of image resolution and scanner field of view.
-Relative barycentric position (2 functions). Relative barycentric position relative to the bounding box of the corresponding skeletal part. Values range from 0 (upper, left) to 1 (lower, right).
• Relative center of mass (2 functions). Similar to the function of the center of gravity, but takes into account the intensity of the hot spot site when calculating the x and y values.
-Relative height. The height and relative width of the hot spot relative to the height of the corresponding skeletal site. The width of the hot spot relative to the width of the corresponding skeletal site.
・ Minimum strength. The minimum intensity calculated from all hot spot elements on the corresponding normalized image.
・ Maximum strength. Maximum intensity calculated from all hot spot elements on the corresponding normalized image.
・ Total strength. The sum of the intensities calculated from all hot spot elements on the corresponding normalized image.
・ Average strength. The average intensity calculated from all hot spot elements on the corresponding normalized image.
・ Standard deviation of intensity. Standard deviation of the intensity calculated from all hot spot elements on the corresponding normalized image.
・ Boundary length. The length of the hotspot boundary measured in pixels.
・ Consistency. The percentage of the convex hull area of the hot spot represented by the hot spot area.
・ Eccentricity. The elongation of the hot spot represented by 0 (circle) to 1 (line).
・ Total number of hot spot counts. Sum of intensities in all hot spots across the skeleton.
• Number of hot spot counts at the site. Sum of hot spot intensities contained in the skeleton corresponding to the current hot spot.
-Total hot spot range. The area of all hot spots in the entire skeletal site relative to the entire skeletal site in the corresponding image.
-Hot spot range at the site. The area of all hot spots in the skeletal site corresponding to the current hot spot for the area of the skeletal site.
・ Total number of hot spots. Number of hot spots across the skeleton.
-The number of hot spots at the site. The number of hot spots in the skeletal site corresponding to the current hot spot.
Hot spot locality (2 functions). The X coordinate ranges from 0 (innermost) to 1 (most distal) with respect to the midline calculated from the reference anatomical structure converted in the step of the shape identification unit 110. The Y coordinate ranges from 0 (best) to 1 (worst). All measures are relative to the corresponding skeletal site.
• Distance asymmetry. Minimum Euclidean distance between the relative center of mass of the current hot spot and the mirror image of the mass of the hot spot in the contralateral skeletal region. It is calculated only for skeleton regions that naturally have corresponding contralateral skeleton sites.
• Range asymmetry. The minimum difference in range between the current hot spot and the range of hot spots in the contralateral skeletal site.
-Strength asymmetry. The minimum difference in intensity between the current hot spot and the intensity of the hot spot in the contralateral skeletal region.

  Hereinafter, a specific example of input to the second ANN unit 160 will be described.

The second ANN unit 160 that determines a patient level diagnosis related to the presence or absence of a metastatic disease uses the 34 features listed below as inputs. All functions used by the second ANN unit 160 are calculated from hot spots classified as having a high transferability by the first ANN unit 140.
・ Total lesions. Sum of skeletal lesions across the skeleton.
-Skull lesions. Sum of skeletal lesions within the skull region.
-Cervical spine lesions. Sum of skeletal lesions in the cervical spine column.
-Thoracic column lesions. Sum of skeletal lesions in the thoracic column.
・ Lumbar spine lesions. Sum of skeletal lesions in the lumbar region.
-Upper limb lesions. Sum of skeletal lesions in the upper limbs.
-Lower limb lesions. Sum of skeletal lesions in the lower limbs.
-Chest lesions. Sum of skeletal lesions in the chest region.
・ Pelvic lesions. Sum of skeletal lesions in the pelvic region.
• Total number of “high” hotspots.
• The number of “high” hot spots in the skull area.
• Number of “high” hot spots in the cervical spine region.
• Number of “high” hot spots in the chest column area.
• Number of “high” hot spots in the lumbar column area.
• Number of “high” hot spots in the upper limb region.
• Number of “high” hot spots in the lower limb area.
• Number of “high” hot spots in the chest area.
• Number of “high” hot spots in the pelvic region.
-Maximum ANN output from the first ANN unit 140 in the skull region.
-Maximum ANN output from the first ANN unit 140 at the cervical vertebra site.
-Maximum ANN output from the first ANN unit 140 at the thoracic vertebra site.
-Maximum ANN output from the first ANN unit 140 at the lumbar region.
-Maximum ANN output from the first ANN unit 140 in the sacral region.
The maximum ANN output from the first ANN unit 140 at the humerus part.
-Maximum ANN output from the 1st ANN part 140 in a clavicle part.
-Maximum ANN output from the first ANN section 140 at the scapula site.
-Maximum ANN output from the first ANN unit 140 in the thigh region.
-Maximum ANN output from the first ANN unit 140 in the sternum region.
-Maximum ANN output from the first ANN unit 140 at the rib site.
The second highest ANN output from the first ANN unit 140 at the rib site.
The third highest ANN output from the first ANN unit 140 at the rib site.
-Maximum ANN output from the first ANN unit 140 in the pelvic region.
The second highest ANN output from the first ANN section 140 at the pelvic region.
The third highest ANN output from the first ANN section 140 at the pelvic region.

  As described above, the shape identifying unit 110 can identify a skeletal part in a bone scintigram regardless of the patient's body shape by deforming a predefined skeleton model according to the bone scintigram. it can. Further, the hot spot detection unit 120 can detect abnormal accumulation by detecting a hot spot that is a region having a count value higher than a predetermined threshold value from the identified skeleton site. The first ANN unit 140 can calculate the transfer possibility for each hot spot. The second ANN unit 160 may calculate a patient's metastasis probability. The patient's metastatic potential is sometimes referred to as the ANN value.

  In the following description, the normalized count value is referred to as a normalized count value. FIG. 10 is a histogram showing the distribution of count values of the bone scintigram before normalization. The distribution of count values of normal bone regions (normal regions) that are healthy skeletal elements differs between the bone scintigram 3010 and the bone scintigram 3020 before normalization. That is, the average count value 3011 of the normal bone region of the bone scintigram 3010 is different from the average count value 3021 of the normal bone region of the bone scintigram 3020. FIG. 11 is a histogram showing the distribution of normalized count values of the normalized bone scintigram. A bone scintigram 3030 with normalized bone scintigram 3010 and a bone scintigram 3040 with normalized bone scintigram 3020 are shown. In the bone scintigram 3030 and the bone scintigram 3040, the distribution of normalized count values of normal bone regions overlap. Further, the average count values 3011 and 3021 of the normal bone region are converted into 1000 in the normalized count value by normalization.

  The hot spot detection unit 120 reads out the imaging condition data corresponding to the normalized image from the modified image storage unit 25 and stores it in the normalized image storage unit 35 in association with the normalized image.

  The hot spot detection unit 120 extracts a healthy skeletal element from which a hot spot is removed from a skeletal part, and normalizes the bone scintigram with an average count value of the healthy skeletal element, thereby not affecting the hot spot. The bone scintigram concentration can be made uniform.

  The normalization unit 30 determines a hot spot transfer possibility that is an output of the first ANN unit 140 using a preset threshold value, classifies the hot spot into a high risk site and a low risk site, and determines the classification result. It may be included in the feature data. The normalization unit 30 may calculate a BSI (bone scan index) that is a ratio of the area of the high-risk part to the area of all the skeletal parts, and include the calculation result in the feature data. In addition, the normalization unit 30 may calculate the number of high-risk parts and include the calculation result in the feature data.

  The normalizing unit 30 may generate a normalized image using another normalization method.

  Hereinafter, a method described in Patent Document 2 will be described as an example of another normalization method performed by the normalization unit 30.

  The normalization unit 30 normalizes the image using the average pixel value of the normal bone tissue of each image. First, the normalization unit 30 uses a bone scintigram histogram and a plurality of threshold values to identify a plurality of high-intensity regions including all abnormal regions (abnormal accumulation) and several normal regions (normal accumulation). Next, the normalization unit 30 calculates an average pixel value of each of the plurality of high-intensity regions in order to determine a transition point corresponding to the threshold pixel value for distinguishing between the abnormal region and the normal region. Next, the normalization unit 30 sorts the plurality of high intensity regions based on the rank of the average pixel value.

  FIG. 12 is a diagram illustrating an example of the relationship between the rank of the average pixel value of the high intensity region and the average pixel value of the high intensity region in the bone scintigram. In this figure, the horizontal axis indicates the rank of the average pixel value of each high intensity region, the vertical axis indicates the average pixel value of each high intensity region, and each point on the curve corresponds to the high intensity region. Next, the normalizing unit 30 detects a transition point at which the slope of the curve changes greatly. The transition point indicates the boundary between the abnormal region and the normal region. In addition, the variation in the average pixel value in the normal area is smaller than that in the abnormal area. Next, the normalization unit 30 selects a high intensity region having an average pixel value higher than the average pixel value of the transition point as an abnormal region. In addition, the normalization unit 30 selects several high-intensity regions having an average pixel value equal to or lower than the average pixel value at the transition point and sets them as normal regions. Here, the normalization unit 30 may select a high-intensity region whose average pixel value is less than or equal to the average pixel value of the transition point and greater than a predetermined threshold as the normal region, or downward from the average pixel value of the transition point. A predetermined number of high intensity regions may be selected.

  Next, the normalization unit 30 selects a predetermined number of normal areas from the transition point to the lower side, and calculates an average value P of average pixel values of the selected normal areas. The predetermined number of areas is, for example, 5. Next, the normalization unit 30 calculates a normalization factor F. F is, for example, k / P. Here, k may be set to 1000 in the normalized count value, for example. Next, the normalization unit 30 generates a normalized image by calculating the product of the normalization factor F and the pixel value of the bone scintigram to obtain the pixel value in the normalized image.

  According to this normalization method, the normalization unit 30 identifies an abnormal region and a normal region that are high-intensity regions, and generates a normalized image by normalizing the bone scintigram based on the average pixel value of the normal region. To do. Thereby, the pixel values of a plurality of bone scintigrams can be aligned without being affected by the abnormal region.

  Hereinafter, the adjustment unit 40 will be described.

  The size of the region of the patient in the captured image varies depending on the detector (gamma camera) to be imaged and the acquisition conditions. The pixel size that is the actual size of the region represented by each pixel in the captured image is represented by (reference field of view / matrix size / magnification ratio). The reference field is the length of one side of the detector field square. The matrix size is the number of pixels corresponding to one side of the reference visual field in the matrix constituting the captured image. The magnification in bone scintigram is usually 1. The imaging condition data includes a pixel size, a reference visual field, and a matrix size. When imaging multiple bone scintigrams, the size of the patient area differs depending on the detector (gamma camera) and acquisition conditions, allowing the reader to determine the actual size of the region between the multiple bone scintigrams displayed. It becomes difficult to compare.

  The adjustment unit 40 reads the normalized image and the imaging condition data corresponding to the normalized image from the normalized image storage unit 35. Thereafter, the adjustment unit 40 performs, for example, enlargement or reduction of the pixel size and change of the matrix size so that the normalized image becomes a specific reference visual field based on the imaging condition data corresponding to the normalized image. An adjusted image is generated, and the adjusted image is stored in the adjusted image storage unit 45. Thereby, the reference visual field of the adjusted image becomes constant.

  The adjustment unit 40 reads out the imaging condition data corresponding to the adjustment image from the corrected image storage unit 25 and stores it in the adjustment image storage unit 45 in association with the adjustment image.

  The specific reference field may be the latest reference field among the reference fields of a plurality of bone scintigrams. The specific reference visual field may be the largest reference visual field among the reference visual fields of a plurality of bone scintigrams. Further, the specific reference visual field may be a preset reference visual field.

  According to the adjustment unit 40, the reference field of view of the plurality of bone scintigrams becomes constant, so that the image interpreter can compare the actual sizes of the regions in the plurality of displayed bone scintigrams.

  Hereinafter, the conversion unit 50 will be described.

  According to the normalization method described above, by displaying the adjusted image using the normalized count value as the density, the density ranges of the pixels in the normal bone region are made uniform. However, even if the adjustment image is displayed as it is, it is not necessarily a density scale suitable for recognition of a normal bone region and a hot spot by an image interpreter. For example, if the normalized count value corresponding to the maximum density is too high, most of the normal bone region is displayed in white, and the image interpreter may not be able to recognize the normal bone region in the displayed bone scintigram.

  Therefore, when displaying the adjusted image, the conversion unit 50 converts the normalized count value of the adjusted image into an appropriate gray scale density.

  The conversion unit 50 determines the scale range so that the specific saturation reference value is converted to the maximum gray scale density. The saturation reference value is a value obtained by multiplying a specific maximum reference value by a predetermined range coefficient. For example, the maximum reference value is determined based on a normalized count value obtained by normalizing a bone scintigram in the above-described captured image database by the above-described normalization method. The maximum reference value is, for example, the same or larger than the maximum normalized count value in the captured image database. The range coefficient is greater than 0 and less than 1. A more preferable saturation reference value is larger than 1000 and smaller than the maximum normalized count value in the captured image database.

  FIG. 13 is a diagram illustrating the scale range. In this figure, the horizontal axis indicates the normalized count value of the pixel of the adjusted image, and the vertical axis indicates the density of the pixel of the adjusted image. Thereby, the density of the pixel whose normalized count value is zero is displayed as zero density. Pixels whose normalized count value is greater than or equal to the saturation reference value are displayed at the maximum density. Pixels having a normalized count value greater than zero and less than the saturation reference value are displayed with a gradation corresponding to the normalized count value, for example, a curve gradation such as a linear gradation or a log gradation.

  According to the conversion unit 50, by interpreting the normalized count value of the adjusted image into an appropriate gray scale density, the image interpreter can easily recognize the normal bone region and the hot spot in the displayed bone scintigram. Can do. Note that the conversion unit 50 may adjust the range coefficient or the saturation reference value based on the operation of the input device 4 by the interpreter.

  Hereinafter, the generation unit 80 will be described.

  The generation unit 80 identifies a patient to be displayed and a plurality of examination dates based on the operation of the input device 4 by a radiogram interpreter. The adjustment image corresponding to the specified patient and the examination date is selected from the plurality of adjustment images in the adjustment image storage unit 45. The production | generation part 80 produces | generates the display screen for displaying on the display apparatus 5, and arrange | positions the selected adjustment image in a display screen according to a time series.

  FIG. 14 is a screen showing the first display mode. The display screen according to the first display mode includes a front image area 8100 that is an area for arranging a front image, and a rear image area 8200 for arranging a rear image. In this example, it is assumed that three adjustment image sets are selected. The generation unit 80 arranges the plurality of front images 8511, 8521, and 8531 in the selected plurality of adjustment image sets in the horizontal direction in the front image region 8100 in the order of the inspection date and time, and sets the plurality of images in the selected adjustment image set. The rear images 8512, 8522, and 8532 are arranged in the horizontal direction in the rear image area 8200 in the order of the inspection date. That is, the newer front image is placed more to the right in the front image area 8100, that is, the newer rear image is placed to the right in the rear image area 8200. At this time, the generation unit 80 aligns the horizontal center of the plurality of arranged front images 8511, 8521, and 8531 with the horizontal center 8101 in the front image region 8100, and arranges a plurality of arranged rear images 8512, 8522. , 8532 in the horizontal direction is aligned with the horizontal center 8201 in the rear image area 8200. The front image area 8100 and the rear image area 8200 may be arranged on the left and right, or may be arranged on the top and bottom. The generation unit 80 may attach an examination date to each of the front image and the rear image in the display screen based on the imaging condition data associated with the adjustment image set. Further, the generation unit 80 may attach the possibility of transfer, the BSI, the number of high-risk parts, etc. to each of the front image and the rear image in the display screen based on the feature data associated with the adjustment image set. .

  According to the first display mode, the image reader can easily change the displayed bone scintigram over time by arranging each of the front image and the rear image in which the influence due to the difference in imaging conditions is reduced in time series. Can grasp.

  FIG. 15 is a screen showing the second display mode. The display screen according to the second display mode includes a pair area 8300 that is an area for arranging an adjustment image set that is a pair of a front image and a rear image. The generation unit 80 arranges the selected adjustment image sets 8610, 8620, 8630 in the horizontal direction in the area 8300 in the order of examination date and time. That is, the newer adjusted image set is arranged more right in the pair area 8300. At this time, the generation unit 80 matches the horizontal center of the plurality of adjusted image sets 8610, 8620, and 8630 arranged with the horizontal center 8301 in the pair region 8300. Here, the adjustment image set 8610 includes a front image 8511 and a rear image 8512, the adjustment image set 8620 includes a front image 8521 and a rear image 8522, and the adjustment image set 8630 includes a front image 8531 and a rear image 8532. Have The generation unit 80 may assign an examination date to each of the adjustment image sets in the display screen based on the imaging condition data associated with the adjustment image set. Furthermore, the generation unit 80 may attach the possibility of transfer, the BSI, the number of high-risk parts, etc. to each of the adjusted image sets in the display screen based on the feature data associated with the adjusted image set.

  According to the second display mode, by arranging a plurality of pairs of the front image and the rear image in which the influence due to the difference in the photographing conditions is reduced according to the time series, the image reader can change the displayed bone scintigram over time. It can be easily grasped.

  The generation unit 80 may select one of the first and second display modes according to the operation of the input device 4 by the image interpreter.

  The generation unit 80 may superimpose the outline of the skeletal part indicated in the feature data on the adjustment image. Further, the generation unit 80 may add different colors to the low risk part and the high risk part in the adjusted image.

  The generation unit 80 attaches feature data such as the possibility of transfer, BSI, and the number of high-risk parts to each adjustment image in the display screen, so that the radiogram interpreter can know the temporal change of the feature data.

  The image processing apparatus 1 may include an analysis unit that analyzes the adjusted image. In this case, the analysis unit may compare specific skeleton parts in the adjusted images obtained by the plurality of examinations using the feature data in the feature data storage unit 36. Further, the analysis unit may use the feature data in the feature data storage unit 36 to associate time series of specific hot spots in the adjusted images obtained by a plurality of examinations. By analyzing the plurality of adjusted images in which the influence due to the difference in the imaging conditions is reduced by the analysis unit, it is possible to improve the accuracy of the analysis as compared with the case of analyzing the plurality of captured images.

  The image processing apparatus 1 may have the above-described captured image database. The image processing apparatus 1 may register a captured image satisfying a predetermined condition among the captured images stored in the captured image storage unit 10 in the captured image database.

  Terminology will be explained. The calculation step and the determination step correspond to, for example, the process S2020. The change step corresponds to, for example, the process S2040. The specific range corresponds to the standard range, for example. The specific value corresponds to the reference evaluation value, for example. The storage unit corresponds to the storage unit 70, for example. The calculation unit, the determination unit, and the change unit correspond to the correction unit 20, for example. The normalizing means corresponds to the normalizing unit 30, for example. The display control means corresponds to the display control unit 60, for example. The changing means corresponds to the correcting unit 20, for example. The specific area corresponds to, for example, a hot spot. The size information corresponds to, for example, imaging condition data. The reference value corresponds to a saturation reference value, for example. The first region corresponds to the front image region 8100, for example. The second region corresponds to the rear image region 8200, for example. An image obtained by imaging the distribution state of the radiopharmaceutical in the body of another subject, another image pair, for example, corresponds to a captured image database. The specific range corresponds to the standard range, for example. The specific value corresponds to the reference evaluation value, for example. The normalization step corresponds to, for example, the process S30. The display control step corresponds to, for example, the process S60.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention. For example, in the above-described embodiment, the target is a bone scintigram, but other images such as CT, MRI, SPECT, PET, and X-rays (X-rays) may be used.

1: Image processing device 4: Input device 5: Display device 10: Image processing device body 15: Captured image storage unit 20: Correction unit 25: Correction image storage unit 30: Normalization unit 35: Normalized image storage unit 36: Features Data storage unit 40: Adjustment unit 45: Adjustment image storage unit 50: Conversion unit 60: Display control unit 70: Storage unit 80: Generation unit

Claims (11)

An image processing program for a computer that stores an image obtained by imaging a distribution state of a radiopharmaceutical in a subject's body,
A calculation step of calculating an evaluation value based on a pixel value in the image;
A determination step of determining whether or not the evaluation value is outside a specific range;
When the evaluation value is outside the specific range, a modified image obtained by changing all pixel values in the image based on the evaluation value is stored, and when the evaluation value is within the specific range, A changing step of storing the image as the modified image ;
A segmentation step for identifying anatomical sites in the modified image;
An image processing program for causing a computer to execute. In the changing step, when the evaluation value is outside the specific range, the pixel value of the image is changed so that the evaluation value of the changed image becomes a specific value within the specific range.
The image processing program according to claim 1. The specific range and the specific value are determined based on a plurality of evaluation values respectively calculated based on a plurality of images.
The image processing program according to claim 2. The calculating step calculates the evaluation value based on a sum of pixel values in the image;
The image processing program according to claim 1. The calculating step calculates the evaluation value based on a sum of pixel values of a specific region in the image.
The image processing program according to claim 1. The image is a bone scintigram and includes a front image and a back image of the subject.
The image processing program according to claim 1. The calculating step calculates the evaluation value based on a pixel value in the front image and a pixel value in the rear image,
In the changing step, when the evaluation value is outside the specific range, the pixel value in the front image and the pixel value in the rear image are changed based on the evaluation value.
The image processing program according to claim 6. Furthermore,
A normalization step of normalizing the plurality of modified image including the modified image,
A display control step of generating a display image by arranging the plurality of normalized images corresponding to the time when the images were captured, and displaying the display image on a display device;
To run on a computer,
The image processing program according to claim 1.   A recording medium storing the image processing program according to claim 1. Storage means for storing an image obtained by imaging the distribution state of the radiopharmaceutical in the subject's body;
Calculating means for calculating an evaluation value based on a pixel value in the image;
Determination means for determining whether or not the evaluation value is outside a specific range;
When the evaluation value is outside the specific range, a modified image obtained by changing all pixel values in the image based on the evaluation value is stored, and when the evaluation value is within the specific range, Changing means for storing the image as the modified image ;
Segmentation means for identifying anatomical sites in the modified image;
An image processing apparatus comprising: An image processing apparatus for storing an image obtained by imaging a distribution state of a radiopharmaceutical in a subject's body,
A calculation step of calculating an evaluation value based on a pixel value in the image;
A determination step of determining whether or not the evaluation value is outside a specific range;
When the evaluation value is outside the specific range, a modified image obtained by changing all pixel values in the image based on the evaluation value is stored, and when the evaluation value is within the specific range, A changing step of storing the image as the modified image ;
A segmentation step for identifying anatomical sites in the modified image;
An image processing method.