JP2007267979A - Method and system of analyzing organ form of living creature - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To automate measurement by obtaining three-dimensional vector data approximate to the shape of an organ of a living creature and using the three-dimensional vector data. <P>SOLUTION: The method of analyzing the organ form of a living creature acquires cross-sectional photographs 11 slicingly taken of the organ of the living creature at a prescribed interval, selects circles or ellipses 14 approximate to the cross-sectional shapes of the organ which are included in the cross-sectional photographs, arranges the circles or the ellipses selected for each cross-sectional photograph in order of photographing and generates vector data 28 showing a solid figure connecting the outside edges of all the circles or ellipses by smooth curved surfaces, acquires feature points 21 included in the cross-sectional shapes of the organ which are included in the cross-sectional photographs, and acquires three-dimensional vector data approximate to the shape of the organ by correcting the vector data 28 to ensure that the outside surface of the solid figure smoothly passes the feature points 21. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a biological organ morphology analysis method and an organ morphology analysis system that can obtain three-dimensional vector data that approximates the shape of a biological organ.

A CT scanning device is used to measure the state of an animal or a human organ. This device enables X-rays and ultrasonic waves to be used for continuous fluoroscopy of cross-sectional photographs of organs, enabling precise measurement of the organs.
Japanese translation of PCT publication No. 2002-527834

Here, the conventional technique has the following problems to be solved.
Organs of living organisms change their shape according to their state. Moreover, the shape is changed by the change of a medical condition. Organs can expand or contract partially or entirely. In addition, for example, like the heart and the lung, it periodically expands and contracts. In order to observe changes in the shape of the organ over time and to precisely observe changes in the shape of the organ due to changes in the medical condition, it has become possible to take more precise cross-sectional photographs. However, it takes a lot of labor and time to carefully compare and verify the results taken with the naked eye and measure subtle changes in the state. There is also a risk of overlooking minute changes.

  The present invention has been made paying attention to the above points, and an object thereof is to provide a biological organ morphology analysis method and an organ morphology analysis system capable of obtaining three-dimensional vector data that approximates the shape of a biological organ. And A further object of the present invention is to provide an organ morphology analysis method and an organ morphology analysis system that can automatically measure changes in the shape of an organ using the three-dimensional vector data.

In each embodiment of the present invention, the above-described problems are solved by the following configurations.
<Configuration 1>
Obtain cross-sectional photographs taken so that the organs of living organisms are sliced at predetermined intervals, select a circle or ellipse that approximates the cross-sectional shape of the organ included in each cross-sectional photograph, and select the circle or ellipse selected for each cross-sectional photograph above The ellipses are arranged in the order in which they were taken, and all the circles or ellipses are connected to each other by a smooth curved surface to generate vector data. The included feature points are acquired, and the vector data is corrected so that the outer surface of the solid figure smoothly passes through the feature points to obtain three-dimensional vector data that approximates the shape of the organ. A method for analyzing organ morphology of a living organism.

  A cross-sectional picture of the organ cut into rounds can be obtained with a CT scanning device. Based on this cross-sectional photograph, three-dimensional vector data of the organ is obtained. Since the approximately generated vector data is corrected based on the feature points, vector data that well represents the imaging target can be generated. Because it is three-dimensional vector data, differential analysis and partial or total advance processing can be easily performed.

<Configuration 2>
A storage device that stores image data of cross-sectional photographs taken so that biological organs are cut at predetermined intervals, and an approximation that generates circle or ellipse vector data that approximates the cross-sectional shape of the organ included in each cross-sectional photograph The circle generation means and the circles or ellipses selected for each of the cross-sectional photographs are arranged in the order of shooting, and vector data representing a three-dimensional figure connecting all the circles or ellipses with smooth curved surfaces is generated. Three-dimensional vector data generation means; feature point acquisition means for acquiring feature point groups included in the cross-sectional shape of the organ included in the cross-sectional photograph; and the outer surface of the three-dimensional figure smoothes the feature points. An organ form of a living body comprising data correcting means for correcting the vector data so as to pass and obtaining three-dimensional vector data approximating the shape of the organ Analysis system.

<Configuration 3>
In the biological organ morphology analysis system according to Configuration 2, the three-dimensional vector data generation means includes an axis curve passing through the axis of each circle or ellipse, and a circle group or ellipse group intersecting the axis curve substantially perpendicularly. An organ morphology analysis system for living organisms characterized by obtaining three-dimensional vector data so as to draw a curved surface.

  In an organ such as a blood vessel, an axial curve is first obtained, and the three-dimensional vector data of the tube centered on the actual curve can be generated as the one that approximates the actual cross-sectional photograph.

<Configuration 4>
In the biological organ morphology analysis system according to Configuration 2, the data correction unit corrects the vector data of each circle or ellipse so that the outer periphery of each circle or ellipse passes through the feature point. An organ morphology analysis system for living organisms characterized by obtaining three-dimensional vector data approximating the shape of the organ.

  If the vector data is corrected so that the outer periphery of the circle or ellipse passes through the feature points, highly accurate three-dimensional vector data can be generated in which the outer peripheral surface of the tube passes through the feature points.

<Configuration 5>
In the biological organ morphology analysis system according to Configuration 4, the feature point acquisition unit acquires a mark given on the stereoscopic image of the biological organ as a feature point, and any one of the cross-sectional photographs An organ morphology analysis system for living organisms, characterized by assigning feature points to the body.

  When a doctor marks a feature portion on a stereoscopic image obtained by a CT scanner or the like, it is processed as a feature point and automatically used for correcting three-dimensional vector data. Therefore, highly accurate three-dimensional vector data can be generated very simply.

<Configuration 6>
In the biological organ morphology analysis system according to Configuration 4, the feature point acquisition unit selects a corresponding position between the contour line representing the cross-sectional shape of the organ and the outer periphery of the circle or ellipse, and calculates a difference value between the two. An organ morphology analysis system for living organisms characterized in that a feature point is a point where the difference value is calculated and the maximum value is obtained.

  Since the difference value between the approximate circle and the actual cross-sectional photograph is acquired and the feature point is automatically set, the captured image can be automatically analyzed at high speed.

<Configuration 7>
In the biological organ morphology analysis system according to Configuration 4, the surface of the stereoscopic image represented by the three-dimensional vector data is divided into regions of triangles, and position information and edge lengths of each triangle are indicated. An organ morphology analysis system for living organisms, comprising a state acquisition unit that generates a state information group based on information.

  If the surface of the stereoscopic image of the three-dimensional vector data is divided into regions by triangular aggregates, it is possible to acquire data that clearly represents the characteristics of the shape of the organ.

<Configuration 8>
The biological organ morphology analysis system according to Configuration 7, wherein the state acquisition unit uses all the feature points as vertices of any of the triangles.

  When the region is divided into triangles having the feature points as vertices, it is possible to obtain data that is convenient for numerical analysis of movement and change of the feature points.

<Configuration 9>
In the biological organ morphology analysis system according to Configuration 7 or 8, state information is obtained for two three-dimensional vector data obtained from cross-sectional photographs taken at different times, and position information and edges of vertices of all triangles are acquired. A biological organ morphology analysis system comprising state comparison means for comparing triangles having a difference value equal to or greater than a certain value and outputting the result of comparison with information indicating the length of the living body.

Since the change in the shape of the organ can be regarded as a change in the shape of the triangle, the change in the shape of the organ can be numerically analyzed by a computer. Moreover, analysis results can be obtained precisely and at high speed.
<Configuration 10>
In the biological organ morphology analysis system according to Configuration 4, a circle or an ellipse that has been given a feature point and corrected so as to pass through the feature point from the three-dimensional vector data generated in the past is a target of correction processing. Organ morphology analysis system for living organisms

  From the three-dimensional vector data generated in the past, a feature point is given and a circle or ellipse corrected so as to pass through the feature point is obtained. Is close to the actual one and easy to correct. Also, feature points have been assigned, and feature point assignment work is facilitated.

  In the present invention, vector data of a circle or an ellipse approximating each part of the organ is first obtained from a cross-sectional photograph of the organ. This is smoothly corrected based on the feature points to obtain precise three-dimensional vector data. Hereinafter, embodiments of the present invention will be described in detail for each example.

FIG. 1 is an explanatory diagram of an organ morphology analysis method for living organisms according to the first embodiment.
The illustrated organ 10 is, for example, a human blood vessel. The organ 10 is imaged so as to be cut at predetermined intervals using an apparatus such as a CT scan. The cross-sectional photographs 11, 12, and 13 are taken continuously at a predetermined interval. A contour line 17 representing the cross-sectional shape of the organ 10 is extracted from the image of this photograph. Since the method may be a known one, a specific description is omitted. What is necessary is just to detect and connect the boundary line where the image density changes discontinuously. The image data of the cross-sectional photograph and the image data of the contour line thus obtained are stored as, for example, bitmap format data.

  Next, circles or ellipses 14, 15, 16 that approximate the shape of the cross-sectional photographs 11, 12, 13 are selected. Although this selection method will be described in detail later, it is preferable to select a circle or an ellipse having a shape and size close to the contour line 17. The contour line 17 has various irregularities according to the cross-sectional shape of the organ. On the other hand, a circle or an ellipse is vector data drawn by a smooth arc. In order to select an approximate circle or ellipse, for example, the radius of the circle that minimizes the difference between the contour line and the circumference may be calculated.

  Next, the selected circles or ellipses 14, 15, 16,. Further, an axis (center coordinate) 25 of each circle is obtained. These central coordinate groups are connected by a smooth curve. That is, an axial curve 26 passing through the axis 25 of each circle is obtained. Thereby, for example, curve data along the central axis of the blood vessel is obtained. This is three-dimensional vector data. Thereafter, a three-dimensional curved surface including a circle group or an ellipse group intersecting the axis curve 26 substantially perpendicularly is drawn. As a result, three-dimensional vector data 28 representing the external shape of the organ 10 is obtained. When the outer peripheries of the circles or ellipses 14, 15, 16 are connected by a smooth curved surface, three-dimensional vector data representing a three-dimensional figure is obtained.

  If the target is a blood vessel, simpler processing is possible. First, an approximate average value of the width and height is obtained from the cross-sectional shape of the organ. A group of perfect circles whose diameter is the average value is arranged along the axial curve 26. Thereby, the blood vessel is approximated by a tube having a perfect circular cross section. The actual shape is approximated by correction using feature points to be performed later. This is sufficient when obtaining 3D vector data for a portion of a blood vessel. This three-dimensional vector data approximates the outer surface of the organ 10 in the example of the figure. What approximates the inner surface may be obtained in the same manner.

  Here, this data is corrected in order to obtain three-dimensional vector data that captures organ features to a degree suitable for precise measurement. First, for example, many feature points are marked on the contour line in each cross-sectional photograph. For example, a doctor may operate a cross-sectional photograph image using a mouse or the like, and set feature points on a portion to be particularly noted. In addition, it is preferable that the computer automatically sets feature points at portions where the shape is greatly different from the approximate circle. As an example, a partially enlarged view of the outline 17 is shown in FIG. There is a large unevenness in a part of the contour line 17. A feature point 21 is set at this vertex. A partially enlarged view of the approximate circle 14 is shown in FIG. A part of the arc 22 is corrected so as to pass through the feature point 21 smoothly. In this way, each approximate circle is corrected. Thereafter, three-dimensional vector data in which the outer surface of the three-dimensional figure surrounding all approximate circles is expressed by a smooth curved surface is generated. This curved surface is formed so as to pass through all the set feature points 21. Thus, three-dimensional vector data approximating the shape of the organ is obtained.

  For organ measurement, image data that can recognize changes in the partial shape of the organ is required. An image taken by CT scanning is accurate, but the unevenness of details is severe, analysis is impossible without the naked eye, and a high level of expertise is required. Also, it is not easy to compare changes over time. As described above, the three-dimensional vector data that expresses an important point in a cross-sectional photograph of an organ, that is, a curved surface that passes through a feature point, is a data that well expresses the feature of an organ and is data that can be easily analyzed by a computer. It is. Therefore, the present invention is particularly suitable for morphological analysis of a biological organ that changes its shape depending on the state. In addition, it is possible to accurately detect a dynamic or static change that changes its shape according to a change in a medical condition, or that partially or wholly expands or contracts. In addition, it is also possible to analyze which part is expanding and contracting periodically and whether the movement is normal when performing periodic expansion and contraction like the heart and lungs. Details thereof will be described in the next embodiment.

FIG. 2 is a functional block diagram of a biological organ morphology analysis system.
This system is realized by installing a predetermined program in the personal computer 30. The personal computer 30 is provided with an arithmetic processing device 40 and a storage device 50. The arithmetic processing unit 40 includes approximate circle generation means 41, three-dimensional vector data generation means 42, feature point acquisition means 43, data correction means 44, state acquisition means 45, state comparison means 46, and the like. Each of these means is a computer program that causes a computer to execute a predetermined process.

  The storage device 50 stores cross-sectional photographic image data 51, approximate circle data 52, three-dimensional vector data 53, feature point data 54, and the like. The personal computer includes a main body control unit 1, a keyboard 2, a mouse 3, and a display 4. A printer 32 is connected to the personal computer 30. An arithmetic processing device 40 and a storage device 50 are built in the main body control unit 1. In the drawing, the functional block is displayed below the main body control unit. The printer 32 is externally attached.

  The user of the above system operates the keyboard 2 and the mouse 3 and looks at a cross-sectional photograph displayed on the display 4 and performs, for example, an operation of inputting feature points. The sectional photograph image data 51 is a group of sectional photographs taken by a CT scanner or the like. This data is image data in a bitmap format or JPEG format. The approximate circle data 52 is vector data widely used in a CAD apparatus or the like. The three-dimensional vector data 53 is data for displaying a stereoscopic image of an organ that can be used in CG or the like. The feature point data 54 is composed of three-dimensional coordinate values. It is input artificially or automatically generated and temporarily stored in the storage device 50. The state information 55 is composed of position information of vertexes of each triangle and information indicating the length of each side when the surface of the stereoscopic image of the organ represented by the three-dimensional vector data 53 is divided into regions of triangles. . The identification symbol 56 is a symbol for specifying all the triangles. The vertex coordinates 57 represent the triangle vertices in three-dimensional coordinates.

  The approximate circle generating means 41 of the arithmetic processing unit 40 has a function of generating circle or ellipse vector data that approximates the cross-sectional shape of the organ included in the cross-sectional photograph. The three-dimensional vector data generating means 42 arranges the circles or ellipses selected for each cross-sectional photograph in the order in which they were photographed, and generates vector data representing a three-dimensional figure in which all the circles or ellipses are connected by smooth curved surfaces. It has a function to do. The feature point acquisition unit 43 has a function of storing the feature point input by operating the mouse 3 or the like in the storage device 50 as the feature point data 54. The data correcting unit 44 has a function of correcting the vector data so that the outer surface of the three-dimensional figure smoothly passes through the feature points to obtain three-dimensional vector data 53 that approximates the shape of the organ.

  The state acquisition means 45 divides the surface of the stereoscopic image represented by the three-dimensional vector data 53 into regions of triangles, and a state information group 55 including position information of vertexes of each triangle and information indicating side lengths. With the ability to generate Furthermore, at this time, it has a function of using all feature points as any vertex of the triangle. Further, the state comparison means 46 acquires state information for two three-dimensional vector data obtained from cross-sectional photographs taken at different times, and obtains position information of vertexes of all triangles and information indicating the lengths of the sides. In comparison, it has a function of selecting a triangle having a certain difference value or more and outputting the result.

FIG. 3 is an explanatory diagram of the operation of the data correction means.
A broken line shown in the figure is an example of an organ outline 61. A solid circle indicates the outer peripheral edge 62 of the approximate circle. First, when obtaining an approximate circle, as shown in FIG. 5A, the area of the portion surrounded by the contour line 61 is calculated to obtain a circle having the same area. Next, a center 63 is set at an arbitrary location, and for example, as shown in the figure, a point that intersects the radius 65 of the circle and the contour line or the outer periphery is obtained. A point 66 is an intersection of the radius 65 of the circle and the outline 61 of the cross-sectional shape. A point 67 is an intersection of the radius 65 of the circle and the outer peripheral edge 62 of the side. Then, the distance between points 66 and 67 is obtained. This distance is calculated over the entire circumference of the circle and accumulated. What is necessary is just to obtain an integral value. In this way, the center coordinate of the circle whose integrated value indicates the minimum value is obtained. Thus, an approximate circle is obtained.

  It should be noted that a circle or an ellipse that substantially overlaps the contour line can be selected by various other known methods. Here, a feature point 64 is set in the characteristic uneven portion of the contour line 61. The number of feature points 64 is arbitrary. Further, as shown in (b) of the figure, for example, the intersection of the radius 65 of the circle passing through the feature point 64 and the outer peripheral edge 62 of the circle is moved to the position of the feature point 64 of the outline 61 to this vicinity. The outer peripheral edge 62 is deformed. At this time, the outer peripheral edge 62 is deformed so that the outer peripheral edge 62 passes through the feature point 64 smoothly. Such a deformation method may be a method well known in a CAD system. As described above, when the outer periphery of the approximate circle is deformed so as to pass through the feature points, the approximate circle sufficiently approaches the cross-sectional shape of the actual organ. In addition, since the obtained data is three-dimensional vector data, it becomes easy to calculate and analyze the solid figure of the organ by various methods using a computer.

FIG. 4 is a hardware block diagram of the system.
The above system can be realized by a computer having a known configuration. Connected to the internal bus 110 of the computer 30 are a CPU (Central Processing Unit) 111, a ROM (Read Only Memory) 112, a RAM (Random Access Memory) 113, an HDD (Hard Disk) 114, and an input / output interface 115. Has been. A display 4, a keyboard 2, a mouse 3, and a printer 32 are connected to the input / output interface 115.

  The storage device 50 illustrated in FIG. 1 includes a ROM 112, a RAM 113, and an HDD 114. The arithmetic processing unit 40 shown in FIG. 1 includes a CPU 111, a ROM 112, a RAM 113, and the like. Various types of information are mainly stored and saved in the HDD 114. A computer program executed by the CPU 111 is stored in the ROM 112 or loaded into the RAM 113 as appropriate. If this computer is connected to a CT scanner or the like via an input / output interface, captured image data can be acquired.

FIG. 5 is a specific external perspective view of an organ.
If feature points are set for each cross-sectional photograph as described above, highly accurate three-dimensional vector data can be obtained. In the example of this figure, the position of the feature point 72 is displayed as a black circle on the external appearance shape 71 of the organ drawn using three-dimensional vector data. In this embodiment, the surface of the organ is divided into regions of triangles 73. It is preferable to use triangles of approximately the same size for area division. It is possible to determine the range of the side length almost and set the vertices and sides automatically by the computer. These triangles are used to confirm changes in the shape of the organ. Therefore, it is preferable that the feature point 72 is included in any one of the vertices of the triangle.

  For example, three-dimensional vector data as shown in this figure is acquired from cross-sectional photographs taken at different timings. Then, state information groups each including an aggregate of triangles 73 are generated. As shown in FIG. 1, the state information 55 of one 3D vector data includes information indicating identification information 56 and vertex coordinates 57 of all triangles divided into regions. For example, in the region dividing method, all the outer peripheral edges of the approximate circle are equally divided by a certain number. Then, as in the example of the figure, the sides of the triangle are regularly determined. For any three-dimensional vector data, a state information group is generated by the same method.

  Thus, the three-dimensional vector data obtained at different timings are compared. As described above, if the sides of the triangle are determined according to certain rules and the region is divided, the number of triangles and the relative positional relationship do not change even if the shape of the organ changes with time. The correspondence of each triangle can be recognized by an identification symbol. Here, the change in the length of the side is detected from the vertex coordinates of the triangle having the same identification symbol. What is necessary is just to obtain | require the difference value of the length of a side. For example, when a part of an organ is deformed by expanding and contracting, a large difference value appears only in some triangles when two pieces of three-dimensional vector data are compared. Thereby, it is possible to automatically extract the shape change of the organ quantitatively. Of course, the doctor may analyze the state and details of the change including other information. When the changed location is found, an actual cross-sectional photograph of the corresponding part is taken out and analyzed with the naked eye.

  In addition, when this three-dimensional vector data is used, it is easy to represent a state in which an organ swells or contracts like a heart with CG (computer graphics). At this time, first, one three-dimensional vector data is generated. Then, the sides of each triangle are expanded and contracted according to a predetermined rule. You can predict which part will be deformed by expanding and contracting. It is also possible to determine which portion has expanded or contracted by observing three-dimensional vector data obtained from a cross-sectional photograph of a deformed organ. Since these deformations can be made automatically by a computer, it becomes possible to analyze organ functions and various disease symptoms in detail and accurately.

FIG. 6 is an explanatory diagram of an image processing method using existing data.
In the above method, when a cross-sectional photograph of an organ portion is taken using an apparatus such as a CT scan, an operation of assigning a number of feature points to the cross-sectional photograph is performed. However, for example, if there is 3D vector data obtained by the same procedure in the past, this can be used. This three-dimensional vector data includes the position information of the feature point group assigned last time. From the three-dimensional vector data, a circle or ellipse vector data group including the feature point group and smoothly passing through the feature point is generated. That is, a procedure just opposite to the procedure for generating the three-dimensional vector data is executed. This is displayed on the display side by side with a cross-sectional photograph of the newly taken organ part. Images may be displayed on the display in an overlapping manner. In the example shown in the figure, the existing vector data 81 and the newly taken cross-sectional photograph outline 82 are displayed in an overlapping manner. When the transformation process is performed so that all the feature points of the existing vector data 81 are moved onto the contour 82 of the cross-sectional photograph, the vector data of the figure that approximates the contour 82 of the cross-sectional photograph and passes through all the feature points is obtained. can get.

  You may add the feature point 84 newly as needed. Necessary feature points can be assigned very quickly compared to the process of assigning all feature points for the first time. Moreover, when the position of any one of the feature points has moved greatly, it is easy to confirm it. Since it is possible to prevent oversight of points to be noted, highly accurate measurement can be performed. Further, for example, there is a method of using a standard model of an organ prepared in advance for comparison. A standard feature point group is assigned to the standard model. From this three-dimensional vector data of the standard model, a circle or ellipse vector data group corresponding to a newly photographed cross-sectional photograph of the organ part is generated. Since this process itself may be performed using a known computer processing technique, a specific description is omitted. The process of FIG. 6 is executed by superimposing the cross-sectional figure of the standard model to which the feature points have been added and the cross-sectional photograph actually taken. This is practical enough. Appropriate feature points can be assigned. In addition, individual characteristics can be accurately analyzed by comparison with a standard model.

FIG. 7 is a flow chart of the three-dimensional vector data generation procedure.
The three-dimensional vector data of the organ is generated by the following procedure.
In step S11, using a CT scan or the like, a cross-sectional photograph of the organ portion that is photographed so that the organ is cut into circles at predetermined intervals is continuously taken.
In step S12, the boundary line of the image is detected to extract a contour line representing the cross-sectional shape of the organ.
In step S13, a circle or an ellipse that approximates the cross-sectional shape of the organ included in each cross-sectional photograph is selected. Specifically, the circle or ellipse having the smallest difference value between the contour line and the circumference is selected.
In step S14, the centers of the approximate circles are connected to obtain an axial curve.
In step S15, the circles or ellipses selected for each cross-sectional photograph are arranged in the order of shooting, and vector data is generated so as to represent a solid figure in which all the circles or ellipses are connected by a smooth curved surface. .
In step S16, a feature point group included in the cross-sectional shape of the organ included in the cross-sectional photograph is acquired. For example, a doctor may input by an operation of plotting important parts using a mouse. Moreover, when calculating the difference value between the contour line and the circumference, it is preferable that the point at which the difference value takes the maximum value is automatically set as the feature point. A temporary feature point group may be automatically set, and a doctor may check and correct the result for optimization.
In step S17, the vector data is corrected so that the outer surface of the three-dimensional figure smoothly passes through all the feature points.
At this time, for example, a technique used in known CAD, in which a part of a circular arc of a base circle is connected by a Bezier curve passing through a feature point, may be employed.
In step S18, three-dimensional vector data approximating the shape of the organ is output.

  The three-dimensional vector data obtained as described above is suitable for computer analysis processing. For example, the computer can accurately compare and process which part is changing with the passage of time. Therefore, it is possible to measure quickly without overlooking subtle changes. In the next embodiment, a comparison calculation processing method for three-dimensional vector data will be described.

FIG. 8 is a flowchart of the comparison calculation processing operation of the three-dimensional vector data.
Two types of three-dimensional vector data are compared in the following procedure.
In step S21, three-dimensional vector data before and after the change is acquired. This can be done by executing the procedure of FIG. 6 twice at different timings.
In step S22, feature points are displayed on the surface of the three-dimensional image data. Of course, the inner surface or the side surface may be used. This is all expressed as a surface.
In step S23, an area division rule is set. A rule may be provided for each type of target organ or for each condition.
In step S24, the region is divided by a triangular aggregate including a triangle having the feature point as one vertex.
In step S25, the corresponding triangle shapes are compared. It is good to calculate the difference value of the side.
In step S26, a triangle whose difference value exceeds the threshold is extracted. The threshold value may be fixed or set each time.
In step S27, a cross-sectional photograph near the extracted triangle is selected.
In step S28, the processing result is output. That is, the fact that there has been a change in the triangle of a specific part is reported, and the number of the cross-sectional photograph in the vicinity thereof is displayed to prepare for the analysis of the corresponding part with the naked eye.
As described above, it is possible to automatically detect a particularly significant part of the organ. The region dividing method, the triangle comparison method, and the like are not limited to the above-described embodiment, and various known methods can be employed.

  Note that the computer program executed by the arithmetic processing unit may be modularized in units illustrated in functional blocks, or may be integrated by combining a plurality of functional blocks. Further, the above computer program may be used by being incorporated into an existing application program. The computer program for realizing the present invention can be recorded on a computer-readable recording medium such as a CD-ROM and installed in any information processing apparatus for use.

It is explanatory drawing of the organ morphologic analysis method of Example 1. FIG. FIG. 2 is a functional block diagram of a biological organ morphology analysis system. It is operation | movement explanatory drawing of a data correction means. It is a hardware block diagram of a system. It is a concrete external appearance perspective view of an organ. It is explanatory drawing of the image processing method using the existing data. It is a three-dimensional vector data generation procedure flowchart. It is a comparison calculation processing operation flowchart of three-dimensional vector data.

Explanation of symbols

10 organ 11, 12, 13 cross-sectional photograph 14, 15, 16 circle or ellipse 17 contour 21 feature point 22 arc 25 axis 26 axis curve 28 three-dimensional vector data

Claims (10)

Acquire cross-sectional photographs taken so that the organs of living organisms are cut at predetermined intervals,
Select a circle or ellipse that approximates the cross-sectional shape of the organ included in each cross-sectional photograph,
While the circle or ellipse selected for each cross-sectional photograph is arranged in the taken order, and generates vector data representing a three-dimensional figure connecting the outer peripheries of all the circles or ellipses with a smooth curved surface,
Acquire feature points included in the cross-sectional shape of the organ included in the cross-sectional photograph,
The vector data is corrected so that the outer surface of the solid figure smoothly passes through the feature points,
A method for analyzing organ morphology of a living organism, comprising obtaining three-dimensional vector data approximating the shape of the organ. A storage device that stores image data of cross-sectional photographs taken so that the organs of living organisms are sliced at predetermined intervals;
Approximate circle generating means for generating circle or ellipse vector data approximating the cross-sectional shape of the organ included in each cross-sectional photograph,
Three-dimensional vector data for generating a vector data representing a three-dimensional figure in which the circles or ellipses selected for each cross-sectional photograph are arranged in the photographed order and all the circles or ellipses are connected by smooth curved surfaces. Generating means;
Feature point acquisition means for acquiring a feature point group included in the cross-sectional shape of the organ included in the cross-sectional photograph;
Data correction means for obtaining three-dimensional vector data that approximates the shape of the organ by correcting the vector data so that the outer surface of the three-dimensional figure smoothly passes through the feature points. Organ morphology analysis system for living organisms. In the organ morphological analysis system according to claim 2,
The three-dimensional vector data generating means draws three-dimensional vector data so as to draw an axis curve passing through the axis of each circle or ellipse and a curved surface including a circle group or ellipse group that intersects the axis curve substantially perpendicularly. An organ morphology analysis system for living organisms characterized in that it is obtained. In the organ morphological analysis system according to claim 2,
The data correction means corrects the vector data of each circle or ellipse so that the outer periphery of each circle or ellipse passes through the feature point, and obtains three-dimensional vector data that approximates the shape of the organ. An organ morphology analysis system for living organisms characterized in that it is obtained. The biological organ morphology analysis system according to claim 4,
The feature point acquisition means acquires a mark given on a three-dimensional image of a biological organ as a feature point, and assigns a feature point to any one of the cross-sectional photographs. Organ morphology analysis system. The biological organ morphology analysis system according to claim 4,
The feature point acquisition means calculates a difference value between the contour line representing the cross-sectional shape of the organ and a corresponding position of the outer periphery of the circle or ellipse, and calculates a difference value between the two points. Organ organ morphology analysis system characterized by feature points. The biological organ morphology analysis system according to claim 4,
A state acquisition unit configured to divide the surface of the stereoscopic image represented by the three-dimensional vector data into a set of triangles, and to generate a state information group based on position information of vertexes of each triangle and information indicating side lengths; Organ morphology analysis system for living organisms. The biological organ morphology analysis system according to claim 7,
The state acquisition means uses all the feature points as vertices of any one of the triangles. The organ form analysis system for living organisms according to claim 7 or 8,
For two three-dimensional vector data obtained from cross-sectional photographs taken at different times, the state information is obtained, and the position information of the vertices of all the triangles is compared with the information indicating the lengths of the sides. A biological organ morphology analysis system comprising state comparison means for selecting a triangle above a certain level and outputting the result. The biological organ morphology analysis system according to claim 4,
A biological organ morphology analysis system characterized in that a circle or an ellipse that is given a feature point and corrected so as to pass through the feature point from the three-dimensional vector data generated in the past is subjected to correction processing. .

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