JP2014000124A - Medical image processor and medical image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a medical image processor, etc. that are capable of creating a cross-sectional image having cross-sectional thickness ununiform at each pixel in a process for creating any cross-sectional image on the basis of three-dimensional volume data.SOLUTION: When a user inputs a sectional position of a cross-sectional image to be created for an observation object in a process for creating and displaying an image of any cross section on the basis of three-dimensional volume data having a plurality of sheets of two-dimensional tomogram images piled up therein, a CPU 101 of a medical image processor 100 calculates the cross-sectional thickness at each pixel in the cross-sectional image on the basis of shape of the observation object. When a plurality of orthogonal cross-sectional images are created along a curved portion like a blood vessel, for example, the cross-sectional thickness at each pixel is calculated on the basis of the positions of adjacent cross sections. The CPU 101 further extracts the pixel value data corresponding to the calculated cross-sectional thickness from the three-dimensional volume data to calculate the pixel value of each pixel in the cross-sectional image.

Description

  The present invention relates to a medical image processing apparatus and a medical image processing method for supporting an operation for setting a point at a desired three-dimensional position for a three-dimensional medical image.

Conventionally, for example, a series of tomographic images taken by a medical image diagnostic apparatus and other image generating apparatuses including an X-ray CT (computed tomography) apparatus, an MRI (magnetic resonance imaging) apparatus, an ultrasonic diagnostic apparatus, a nuclear medicine diagnostic apparatus, etc. A medical image processing apparatus that generates and displays a three-dimensional image using an image group is known. Patent Document 1 discloses image processing for reconstructing a cross-sectional image at a cross-sectional position and thickness according to a diagnostic purpose based on a three-dimensional image (three-dimensional volume data) formed by stacking a plurality of tomographic images. The device is described. In the image processing apparatus disclosed in Patent Document 1, when a user inputs a desired cross-sectional position and cross-sectional thickness, a number of tomographic images corresponding to the input cross-sectional thickness are extracted from a base three-dimensional image, and a plurality of these It is described that one cross-sectional image is reconstructed in real time by adding pixel values of each pixel of the tomographic image.
In addition, as a technique for reconstructing a cross-sectional image based on a three-dimensional image, techniques such as MPR (Multi Planer Reconstruction) and CPR (Curved Planer Reconstruction) are proposed. MPR is a technique for reconstructing a cross-sectional image at a position arbitrarily designated by a user based on a three-dimensional image. CPR is a technique for reconstructing an image of a cut surface along a core line of a luminal organ such as a blood vessel based on a three-dimensional image.

JP 2001-87229 A

  However, as described in Patent Document 1 described above, conventionally, the cross-sectional thickness of a cross-sectional image created based on a three-dimensional image (three-dimensional volume data) is uniform for each pixel. For this reason, when the site to be observed is curved like a blood vessel, for example, a portion that is not reflected in the reconstructed cross-sectional image may be formed depending on how the cross-sectional position and cross-sectional thickness are set. Specifically, for example, when reconstructing a plurality of orthogonal cross-sectional images for a site where a blood vessel is greatly curved, if the cross-sectional thickness is reduced, the pixel corresponding to the outside of the curve of each orthogonal cross-section is the original three-dimensional It may not be possible to reflect all image data. Therefore, all pixel value data to be observed is not reflected in the cross-sectional image, and important lesions and the like may be missed.

  The present invention has been made in view of the above problems, and in the process of creating a two-dimensional cross-sectional image at an arbitrary position based on three-dimensional volume data, a cross-sectional image with a non-uniform cross-sectional thickness at each pixel is obtained. It is an object of the present invention to provide a medical image processing apparatus and a medical image processing method that can generate a two-dimensional cross-sectional image that reflects all necessary data according to the shape of an observation target.

  In order to achieve the above-described object, the first invention is a medical image processing apparatus for creating and displaying a cross-sectional image at an arbitrary position based on three-dimensional volume data obtained by stacking a plurality of tomographic images, A cross-sectional thickness setting means for setting a non-uniform cross-sectional thickness for each pixel of the cross-sectional image, and pixel value data corresponding to the cross-sectional thickness set by the cross-sectional thickness setting means is extracted from the volume data and extracted. Pixel value calculation means for calculating the pixel value of each pixel of the cross-sectional image based on the pixel value data, and display means for creating and displaying a cross-sectional image composed of the pixel values calculated by the pixel value calculation means, A medical image processing apparatus.

  A second invention is a medical image processing method for creating a cross-sectional image at an arbitrary position based on three-dimensional volume data obtained by stacking a plurality of tomographic images, wherein the cross-sectional thickness is not uniform for each pixel of the cross-sectional image. A cross-sectional thickness setting step for setting the cross-sectional thickness, and pixel value data corresponding to the cross-sectional thickness set by the cross-sectional thickness setting step is extracted from the volume data, and each of the cross-sectional images based on the extracted pixel value data A medical image processing method comprising: a pixel value calculating step for calculating a pixel value of a pixel; and a display step for creating a cross-sectional image composed of the pixel values calculated in the pixel value calculating step.

  According to the medical image processing apparatus and the medical image processing method of the present invention, in a process of creating a two-dimensional cross-sectional image at an arbitrary position based on three-dimensional volume data, a cross-sectional image in which the cross-sectional thickness is not uniform is generated at each pixel. This makes it possible to create a two-dimensional cross-sectional image that reflects all necessary data according to the shape of the object to be observed.

The figure which shows the whole structure of the medical image processing apparatus 100 The flowchart explaining the flow of the orthogonal cross-section image creation process of 1st Embodiment The figure explaining how to calculate the cross-sectional thickness when creating a cross-sectional image orthogonal to the object to be curved The flowchart explaining the flow of the CPR cut surface image creation process of 2nd Embodiment The figure explaining the CPR cut surface image 34 of a luminal organ The figure explaining how to calculate the cross-sectional thickness of each pixel of the CPR cut surface image 34 of the luminal organ

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
[First Embodiment]
First, the configuration of an image processing system 1 to which the medical image processing apparatus 100 of the present invention is applied will be described with reference to FIG.

  As shown in FIG. 1, the image processing system 1 includes a medical image processing apparatus 100 having a display device 107, a mouse 108, and an input device 109, and an image database 111 connected to the medical image processing apparatus 100 via a network 110. And a medical image photographing device 112.

The medical image processing apparatus 100 is a computer that performs processing such as image creation and image analysis.
As shown in FIG. 1, the medical image processing apparatus 100 includes a CPU (Central Processing Unit) 101, a main memory 102, a storage device 103, a communication interface (communication I / F) 104, a display memory 105, and an interface with an external device. (I / F) 106 is provided, and each unit is connected via a bus 113.

  The CPU 101 calls a program stored in the main memory 102 or the storage device 103 to the work memory area on the RAM of the main memory 102 and executes the program, drives and controls each unit connected via the bus 113, and performs medical image processing. Various processes performed by the apparatus 100 are realized.

  Further, the CPU 101 creates a cross-sectional image at an arbitrary position based on the three-dimensional volume data in a cross-sectional image creation process described later. At this time, the cross-sectional thickness of the cross-sectional image is not uniform for each pixel, and a cross-sectional thickness is given to each pixel. For example, the CPU 101 calculates and sets the cross-sectional thickness of each pixel of the cross-sectional image according to the shape of the observation target. Details of the cross-sectional image creation process will be described later.

  The main memory 102 includes a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The ROM permanently holds a computer boot program, a program such as BIOS, data, and the like. The RAM temporarily stores programs, data, and the like loaded from the ROM, the storage device 103, and the like, and includes a work area used by the CPU 101 for performing various processes.

  The storage device 103 is a storage device that reads and writes data to and from an HDD (hard disk drive) and other recording media, and stores a program executed by the CPU 101, data necessary for program execution, an OS (operating system), and the like. . As for the program, a control program corresponding to the OS and an application program are stored. Each of these program codes is read by the CPU 101 as necessary, transferred to the RAM of the main memory 102, and executed as various means.

The communication I / F 104 includes a communication control device, a communication port, and the like, and mediates communication between the medical image processing apparatus 100 and the network 110. The communication I / F 104 controls communication with the image database 111, another computer, or a medical image photographing apparatus 112 such as an X-ray CT apparatus or an MRI apparatus via the network 110.
The I / F 106 is a port for connecting a peripheral device, and transmits / receives data to / from the peripheral device. For example, a pointing device such as a mouse 108 or a touch pen may be connected via the I / F 106.

  The mouse 108 has a button switch and a sensor for detecting the displacement, and outputs position displacement data, button switch input data, and the like to the CPU 101 via the I / F 106.

  The display memory 105 is a buffer that temporarily accumulates display data input from the CPU 101. The accumulated display data is output to the display device 107 at a predetermined timing.

  The display device 107 includes a display device such as a liquid crystal panel and a CRT monitor, and a logic circuit for executing display processing in cooperation with the display device, and is connected to the CPU 101 via the display memory 105. The display device 107 displays display data stored in the display memory 105 under the control of the CPU 101.

  The input device 109 is an input device such as a keyboard, for example, and outputs various instructions and information input by the operator to the CPU 101. The operator interactively operates the medical image processing apparatus 100 using external devices such as the display device 107, the input device 109, and the mouse 108.

  The network 110 includes various communication networks such as a LAN (Local Area Network), a WAN (Wide Area Network), an intranet, and the Internet, and communication connection between the image database 111, a server, other information devices, and the medical image processing apparatus 100. Mediate.

  The image database 111 accumulates and stores image data photographed by the medical image photographing device 112. In the image processing system 1 shown in FIG. 1, the image database 111 is configured to be connected to the medical image processing apparatus 100 via the network 110, but the image database 111 is provided in, for example, the storage device 103 in the medical image processing apparatus 100. You may do it.

Next, the operation of the medical image processing apparatus 100 will be described with reference to FIGS.
In the first embodiment, a process for creating an image of an orthogonal cross section set along a curve (curvature) to be observed will be described.

  The CPU 101 of the medical image processing apparatus 100 reads the program and data related to the cross-sectional image creation process of FIG. 2 from the main memory 102, and executes processing based on this program and data. In addition, when the execution of the following processing is started, image data to be calculated is acquired from the image database 111 or the like via the network 110 and the communication I / F 104 and stored in the storage device 103 of the medical image processing apparatus 100. And

  In the cross-sectional image creation process shown in FIG. 2, first, when the operator operates the mouse 108, the keyboard (input device 109), and the like to select a series of tomographic image data to be observed, the CPU 101 of the medical image processing apparatus 100. Reads the selected tomographic image data as input image data (step S101). Suitable examples of the input image data include a CT image, an MR image, or an ultrasonic image.

  Next, the CPU 101 creates three-dimensional volume data of the organ region from the two-dimensional tomographic image data acquired in step S101 (step S102), and displays it on the display device 107 as the three-dimensional image 20 (step S103).

Next, the CPU 101 receives an input of a cross-sectional position (step S104).
In the cross-section position input process, the CPU 101 first analyzes a part to be observed in the displayed three-dimensional image 20 and designates the center line 21 according to the curve of the part to be observed. The center line 21 is a line serving as a reference for a cross section to be created, and may be a line connecting a plurality of points designated by the user, or may be determined by the CPU 101 based on changes in peripheral pixel values. When the observation target is a luminal organ such as a blood vessel, the CPU 101 may extract a blood vessel region based on changes in peripheral pixel values, further extract a blood vessel core line, and use it as the center line 21. Furthermore, the CPU 101 accepts designation of a cross-sectional position. In the case where a plurality of orthogonal cross-sectional images are continuously created in a section having a region to be observed, input of the start position and end position of the section and the section interval is accepted. The CPU 101 determines a plurality of cross-sectional positions along the center line 21 based on the input start position and end position and the cross-sectional interval. The designation of the cross-sectional position does not specify the section and the cross-sectional interval as described above, but the user may designate each cross-sectional position one by one with a mouse or the like.
When the observation target is twisted, if the center line 21 is set along the twist, an orthogonal cross section with respect to the center line 21 can be set similarly to the above-described processing.

  When the cross-sectional position is designated, the CPU 101 sets the cross-sectional thickness for each pixel in the orthogonal cross-sectional image to be created (step S105). The cross-sectional thickness is calculated based on the shape of the observation target.

Here, the cross-sectional thickness calculation process will be described with reference to FIG.
In FIG. 3A, a center line 21 is designated along a curve (curve) of a region to be observed, and a plurality of orthogonal cross sections (A cross section 24, B cross section 23, C, perpendicular to the center line 21). An example is shown in which the cross-sectional position of the cross-section 25) is specified. Hereinafter, the process of creating the B cross section 23 will be described.
On both sides of the B cross section 23, the cross section positions of two cross sections (A cross section 24 and C cross section 25) are designated.
First, the CPU 101 sets an intermediate cross section A-B 26 between the B cross section 23 to be created and the adjacent A cross section 24. The intermediate cross section A-B 26 is equidistant from the A cross section 24 and the B cross section 23. Similarly, an intermediate section B-C27 is also set between the B section 23 and the adjacent C section 25.

As shown in FIG. 3B, the CPU 101 obtains a cross-sectional thickness d _{i, j} of each pixel P _{i, j} in the B cross-section 23. Hereinafter, for description, the position on the three-dimensional volume data corresponding to the pixel P _{i, j} of the cross-sectional image of the B cross-section 23 is referred to as Q _{i, j} . i is an integer from 0 to m, and j is an integer from 0 to n.
Pixel _{P i, j} of the cross-sectional thickness _{d i, j} is the pixel _{P i,} points _{Q i} on B cross section 23 corresponding to _j, straight line perpendicular to the B section 23 extending from the _j intermediate section A-B26 and It is set as the distance between the points that intersect with the intermediate cross section B-C27.
In this way, the CPU 101 calculates the cross-sectional thickness for each pixel in the B cross-section 23. Then, in the pixel on the orthogonal cross section corresponding to the outer position of the curve, the cross section thickness is set larger than that of the pixel on the orthogonal cross section corresponding to the inner position of the curve.

Next, the CPU 101 calculates the pixel value of each pixel on the B cross section 23. For example, to calculate the pixel value of the pixel _{P i, j,} CPU 101 extracts the pixel _{P i, j} of the cross-sectional thickness _{d i,} 3-dimensional volume data on which to base the amount of the pixel value data corresponding to the _j ( In step S106, the pixel value of each pixel _{Pi, j} is calculated based on the extracted pixel value data (step S107).
Specifically, the pixel value of each pixel _{Pi, j} is, for example, an average value of pixel value data extracted from three-dimensional volume data. Alternatively, the maximum value or the minimum value among the extracted pixel value data may be used as the pixel value of each pixel of the B cross section 23.

When the average value of the extracted pixel value data is used as the pixel value of each pixel _{Pi, j} of the B section 23, all the pixel value data between the intermediate sections A-B26 and B-C27 are stored. It can be reflected in one cross-sectional image. For this reason, pixel value data of the outer portion of the curve, which is often overlooked when the cross-sectional thickness is uniform, can be reflected in the cross-sectional image without omission.
In addition, when the original two-dimensional image data is a CT image, if the maximum value among the pixel value data extracted from the three-dimensional volume data is the pixel value of each pixel of the B cross section 23, the CT value of the blood vessel or the like is high. The part can be highlighted. Further, if the minimum value is the pixel value of each pixel of the B cross section 23, a portion having a low CT value such as air can be highlighted.
As described above, it is desirable that the calculation method of the pixel value of each pixel of the B cross section 23 can be selected according to the observation target, the diagnostic purpose, and the type of the original image (type such as CT image or MR image). .

Further, when calculating the pixel value of each pixel P _{i, j} on the B cross section 23, it corresponds to the distance from the corresponding point Q _{i, j} on the B cross section 23 to the position of the pixel value data to be referenced in the three-dimensional space. A weight may be applied. At this time, a weight closer to the B cross section 23 may be weighted, or a distance farther away may be weighted. By comparing the cross-sectional image created by weighting the near side with the cross-sectional image created by weighting the far side, it is easy to determine what pixel value region is in which part of the cross-sectional thickness Become.

When the pixel values are calculated for all the pixels P _{i, j} on the B cross section 23, the CPU 101 creates a cross-sectional image having the calculated pixel values and displays it on the display device 107 (step S108). When the process proceeds to the next cross-section creation process, steps S105 to S108 are repeated.

As described above, when the medical image processing apparatus 100 creates a cross-sectional image based on the three-dimensional volume data, the cross-sectional thickness of the cross-sectional image is not uniform for each pixel, but is calculated based on the shape of the observation target. To do. For this reason, it is possible to create a cross-sectional image that perfectly reflects the data to be observed. In particular, in the first embodiment, when creating a plurality of orthogonal cross-sectional images along the curvature of the organ, the cross-sectional thickness of each pixel is calculated based on the distance from the adjacent orthogonal cross-section. The data of the part having the outer position of can be included in the cross-sectional thickness of the corresponding pixel in the orthogonal cross-section, and can be reflected without exception.
Further, since an orthogonal cross-sectional image along the designated center line 21 can be created, the present invention can also be applied when the observation target is twisted.

In the above description, the example in which the cross-sectional thickness of each pixel of the target cross-section is obtained based on the position of the adjacent cross-section when calculating the cross-sectional thickness d _{i, j} is described. In other words, both the front and rear data in the cross-sectional thickness direction are reflected in the cross-sectional image. In this way, after creating an image reflecting the data on both the front and rear sides of the cross section, if you want to check whether the target pixel such as a lesion is included on the front or rear side of the cross section image, The pixel value data for only one side may be extracted from the volume data to obtain the pixel value of each pixel _{Pi, j} .
That is, in the example of FIG. 3A, when the CPU 101 obtains the cross-sectional thicknesses d _{i, j} of the pixels P _{i, j} on the B cross-section 23, the points Q _{i, j} and Q on the corresponding B cross-section 23 are obtained. The distance between a point extending from _{i, j} and perpendicular to the B section 23 intersects the intermediate section A-B26 is defined as a section thickness d _{i, j,} and the volume between the B section 23 and the intermediate section A-B26 The pixel value of the pixel P _{i, j} may be obtained using data (pixel value data). Conversely, the distance between the point Q _{i, j} on the B cross section 23 and the point extending from the point Q _{i, j} and perpendicular to the B cross section 23 intersects the intermediate cross section B-C27 is the cross section thickness d _{i, j} . You may make it obtain | require the pixel value of pixel _{Pi, j} using the volume data (pixel value data) between B cross section 23 and intermediate | middle cross section B-C27.

  In this way, in addition to the cross-sectional image reflecting the data on both sides before and after the cross section to be created, if one-side cross-sectional image reflecting the data on one side (front or rear) is further created, the two can be compared. It becomes easy to compare which density value distribution is on which side of the cross-section, and it becomes easy to narrow down a point of interest such as a lesion and observe it.

[Second Embodiment]
Next, a second embodiment of the present invention will be described with reference to FIGS.
The hardware configuration of the medical image processing apparatus 100 of the second embodiment is the same as that of the first embodiment. Hereinafter, the description which overlaps with 1st Embodiment is abbreviate | omitted, and the same each part shall be demonstrated using the same code | symbol.

In the second embodiment, creation of a cut surface (hereinafter referred to as a CPR cut surface) image along a core line of a luminal organ such as a blood vessel will be described.
In the second embodiment, when creating an image of the CPR cut surface 34, the CPU 101 determines the cross-sectional thickness of each pixel according to the shape of the object to be observed without making it uniform.
Hereinafter, the cross-sectional image creation process for the CPR cut surface according to the second embodiment will be described with reference to FIGS. 4 to 6.

  In the cross-sectional image creation process shown in FIG. 4, first, as in the first embodiment, when the operator selects a series of tomographic image data 31a to 31h, the CPU 101 of the medical image processing apparatus 100 selects the selected one. The tomographic image data 31a to 31h are read as input image data (step S201), a three-dimensional image 30 of the organ region is created (step S202), and displayed on the display device 107 (step S203).

Next, the CPU 101 performs an extraction process of an organ to be observed and a core line extraction process from the volume data (step S204).
FIG. 5 is a diagram showing three-dimensional volume data (three-dimensional image) 30 created from input tomographic image data 31a to 31h and a blood vessel 35 to be observed. Since the CT value in the blood vessel 35 is higher than that in other portions, the region of the blood vessel 35 as shown in FIG. 5 can be extracted from the original volume data 30 based on the change in the CT value. Further, the core line of the blood vessel 32 can also be extracted.
Next, the CPU 101 designates the CPR cut surface 34 along the core wire 32 (step S205). In the process of specifying the cut plane 34, the CPU 101 extracts the core line 32 of the luminal organ to be observed based on the volume data, and receives an input of the range of the cut plane image to be created. In the example of FIG. 5, for example, the range of the cut surface image in the range from the point 32 a to the point 32 h on the core line is set. Points 32a, 32b,..., 32h are intersections of the tomographic images 31a, 31b,.
The CPU 101 performs processing from step S205 on for the input range, and creates a CPR cut surface image.

  When the cut surface 34 is designated, the CPU 101 calculates the cross-sectional thickness for each pixel in the CPR cut surface image 34 to be created (step S206). The cross-sectional thickness is the thickness of the blood vessel 35 to be observed. The thickness of the blood vessel 35 has been calculated by the organ extraction process in step S204.

Next, the CPU 101 extracts pixel value data corresponding to the calculated cross-sectional thickness d _{i, j} for each pixel P _{i, j} of the CPR cut surface image 34 from the base three-dimensional volume data 30 (step S207). Based on the extracted pixel value data, the pixel value of each pixel of the CPR cut surface image 34 is calculated (step S208).

The calculation of the cross-sectional thickness d _{i, j} will be described with reference to FIG.
In FIG. 6, a position in the three-dimensional space corresponding to the pixel P _{i, j} of the CPR cut surface image 34 is defined as a point Q _{i, j} . When calculating the pixel value of the pixel P _{i, j} , the CPU 101 passes through the point Q _{i, j} corresponding to the pixel P _{i, j} , and is a straight section thickness d _{i, j} corresponding to the CPR cut surface 34. Is extracted from the three-dimensional volume data, and for example, the average value of the extracted pixel value data is set as the pixel value of the pixel P _{i, j} of the CPR cut plane image 34.

Alternatively, as in the first embodiment, the maximum value or the minimum value of the extracted pixel value data may be used as the pixel value of each pixel P _{i, j} of the CPR cut surface image 34. It is desirable that the pixel value calculation method can be selected according to the observation target, the diagnostic purpose, and the type of the original image.
Further, when calculating the pixel value of each pixel P _{i, j on} the CPR cut surface, it corresponds to the distance from the corresponding point Q _{i, j} on the CPR cross-sectional image 34 to the position in the three-dimensional space of the referenced pixel value data. You may make it apply the weight. At this time, the weight closer to the CPR cut surface 34 may be weighted, or the distance farther away may be weighted. By comparing the cross-sectional image created by weighting the near side with the cross-sectional image created by weighting the far side, it is easy to determine what pixel value region is in which part of the cross-sectional thickness Become.

When the pixel values are calculated for all the pixels P _{i, j} on the CPR cut surface 34, the CPU 101 creates a cross-sectional image having the calculated pixel values and displays it on the display device 107 (step S209). Since the cross-sectional thickness of each pixel in the CPR cut surface image 34 is the thickness of the lumen at that position, the cross-sectional thickness of the pixel corresponding to the outer position of the lumen is “0”. Therefore, for the pixels corresponding to the outer region of the lumen, a user-specified or default value may be given as the pixel value.

As described above, according to the processing of the second embodiment, when the CPR cut surface image 34 is created along a luminal organ such as the blood vessel 35, the cross-sectional thickness of each pixel is not uniform. The thickness of the lumen to be observed. As a result, it is possible to reflect all the volume data inside the lumen in the CPR cut surface image 34.
In the description of FIGS. 5 and 6, when the cross-sectional thicknesses _{di, j} of each pixel of the CPR cut surface image 34 are obtained, the front and rear lumen regions in the thickness direction with respect to the CPR cut surface image 34 are indicated. The cross-sectional thickness is determined. However, when it is desired to confirm whether the attention pixel such as a lesion is included on the front side or the rear side of the CPR cut surface image 34, only the front side or the rear side of the CPR cut surface image 34. Alternatively, the thickness of only the lumen may be obtained as the cross-sectional thickness d _{i, j} , and pixel value data for only one side may be extracted from the volume data to obtain the pixel value of each pixel P _{i, j} .
In this way, if a single-side cross-sectional image reflecting the volume data on one side is also created together with the cross-sectional image reflecting the volume data on both sides of the CPR cut surface image 34 to be created, the two can be compared. It becomes easy to compare which density value distribution is on which side of the cross section, and it becomes easy to narrow down the position of a lesion or the like.

  The preferred embodiments of the medical image processing apparatus according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

1. Image processing system 100 Medical image processing apparatus 101 CPU
102... Main memory 103... Storage device 104 .. Communication I / F
105 ... Display memory 106 ... I / F
107... Display device 108 mouse 109 input device 110 network 111 image database 113 image photographing device 20・ ・ ・ ・ 3D volume data 21 ・ ・ ・ ・ ・ ・ Center line 23 ・ ・ ・ ・ ・ ・ B section (cross section perpendicular to the center line 21)
24 ··· A cross section (cross section perpendicular to the center line 21)
25 ··· C section (cross section perpendicular to the center line 21)
26 ・ ・ ・ ・ ・ ・ Intermediate section A-B
27 ・ ・ ・ ・ ・ ・ Intermediate section B-C
P _{i, j} ... Cross section image pixel d _{i, j} ... Cross section image pixel P _{i, j} Cross section thickness Q _{i, j.} A point 30 in the three-dimensional space corresponding to P _{i, j} ... 3D volume data 31a to 31h... 2D tomographic image 32. Cut surface 35 ... Blood vessel to be observed

Claims (7)

A medical image processing apparatus for creating and displaying a cross-sectional image at an arbitrary position based on three-dimensional volume data obtained by stacking a plurality of tomographic images,
Cross-sectional thickness setting means for setting a non-uniform cross-sectional thickness for each pixel of the cross-sectional image,
Pixel values for extracting pixel value data corresponding to the cross-sectional thickness set by the cross-sectional thickness setting means from the volume data, and calculating pixel values for each pixel of the cross-sectional image based on the extracted pixel value data A calculation means;
Display means for creating and displaying a cross-sectional image composed of pixel values calculated by the pixel value calculating means;
A medical image processing apparatus comprising:   The medical image processing apparatus according to claim 1, wherein the cross-sectional thickness setting unit calculates a cross-sectional thickness of each pixel of the cross-sectional image according to a shape of an observation target. When creating images of multiple orthogonal sections set along the curvature of the part to be observed,
The cross-sectional thickness setting means sets an intermediate cross section between an orthogonal cross section to be created and an adjacent orthogonal cross section, and calculates a distance from each pixel position of the image of the cross section to be created to the intermediate cross section, respectively. The medical image processing apparatus according to claim 1, wherein the calculated distance is set as a cross-sectional thickness of each pixel. When creating a cross-sectional image along the lumen core,
The cross-sectional thickness setting means calculates the thickness of the lumen at each pixel position of the cut surface image, and sets the calculated thickness of the lumen as the cross-sectional thickness of each pixel of the cut surface image. The medical image processing apparatus according to claim 1 or 2.   The pixel value calculation means uses any one of an average value, a maximum value, and a minimum value of pixel value data corresponding to the cross-sectional thickness as a pixel value of each pixel of the cross-sectional image. The medical image processing apparatus according to any one of claims 1 to 4.   The said pixel value calculation means weights according to the distance from the said pixel position with respect to the said pixel value data referred when calculating the pixel value of each pixel. A medical image processing apparatus according to any one of the above. A medical image creation method for creating a cross-sectional image at an arbitrary position based on three-dimensional volume data obtained by stacking a plurality of tomographic images,
A cross-sectional thickness setting step for setting a non-uniform cross-sectional thickness for each pixel of the cross-sectional image;
Pixel value data corresponding to the cross-sectional thickness set by the cross-sectional thickness setting step is extracted from the volume data, and a pixel value for calculating the pixel value of each pixel of the cross-sectional image based on the extracted pixel value data A calculation step;
A display step of creating a cross-sectional image composed of the pixel values calculated by the pixel value calculation step;
A medical image processing method comprising: