JPWO2015052922A1 - Image processing apparatus, image processing method, and computer program - Google Patents

Abstract

Provided is a technique for identifying a blood vessel that becomes a path from an aorta to a tumor or the like by performing image processing on a tomographic image of a subject. An image forming unit that creates an entire region as 3D image data from a plurality of tomographic images, a main region extracting unit that extracts a main region connected to the aorta from the entire region, and an aortic region that indicates only the aorta from the main region An aorta region extraction unit, an affected region extraction unit for obtaining an affected region including the affected region from all the regions, and a target blood vessel extraction unit for obtaining a target blood vessel region connecting the affected region to the aorta region Image processing device.

Description

  The present invention relates to an image processing apparatus, an image processing method, and a computer program for subject tomography three-dimensional images such as human tomography three-dimensional images. Specifically, for example, the tomographic three-dimensional image taken by computed tomography (CT) or nuclear magnetic resonance imaging (MRI) was image-processed to branch from the aorta. The present invention relates to a technique for specifying a path of a blood vessel that connects a tumor, a bleeding part, and the like to an aorta.

  As one of the treatment methods for patients with malignant tumor (cancer), tumor growth is achieved by injecting an anticancer drug into the aorta using a catheter and sending the anticancer drug to the tumor that leads to the blood vessels branched from the aorta. A method for suppressing the above is known. However, according to such a method, the anticancer agent is sent not only to the tumor cells but also to the cells of the normal organ without the tumor, and the normal cells are damaged by the side effects of the anticancer agents. There was a problem that. As a method for solving such a problem, a method of specifying a path of a blood vessel connecting an aorta and a tumor and delivering an anticancer agent only from the aorta to the tumor via only the blood vessel has been attempted.

  Moreover, when traumatic hemorrhage etc. generate | occur | produce, the deterioration of the symptom of a sick person can be suppressed by specifying the blood vessel connected to the aorta from the bleeding part of a blood vessel, and giving a hemostatic treatment early.

  By the way, the blood vessel has a complex network structure. Moreover, the network structure has a great individual difference. For this reason, it has been difficult to find a blood vessel that connects the aorta and the tumor, or to specify a blood vessel path that connects the aorta and the bleeding source. Conventionally, a medical staff such as a trained radiographer has taken time to read a blood vessel connecting an aorta and a tumor from a tomographic three-dimensional image. However, due to the shortage of medical workers, it is difficult to nurture many human resources with such skills in the medical field. Therefore, there has been a demand for a technique for finding blood vessels that connect the aorta and tumor more efficiently, regardless of individual skills.

  A method for extracting a target blood vessel structure from image data by an image processing technique has already been proposed. For example, Patent Document 1 detects a region having an image characteristic of a target blood vessel structure from image data, and divides a thin line obtained by thinning the detected region by a branch point, a predetermined distance, and the like. It is disclosed to extract a blood vessel tree structure by creating a graph and fitting a shape model of a tree structure representing a general shape of a target blood vessel structure to the created graph. .

JP2011-98195A

  The present invention provides a technique that can efficiently identify a blood vessel connecting a tumor or blood vessel bleeding part and an aorta by performing image processing on a tomographic three-dimensional image of a subject without depending on the individual skill of a medical worker. The purpose is to do.

That is, an image processing apparatus according to one aspect of the present invention provides:
An image forming unit that creates an entire area to be 3D image data from a plurality of tomographic images;
A main region extracting unit for extracting a main region connected to the aorta from the entire region, and an aortic region extracting unit for extracting an aortic region indicating only the aorta from the main region;
An affected area extraction unit for obtaining an affected area including the affected area from the entire area;
And a target blood vessel extraction unit that obtains a target blood vessel region that connects the affected area to the aorta region.

  According to the image processing apparatus of the present invention, a three-dimensional image composed of a tomographic image group composed of a plurality of tomographic images obtained by tomographic imaging of a target region including a tumor (or a bleeding part) and an aorta of a subject is obtained. The entire area is set, and a series of image processing is executed on the entire area.

  Specifically, for example, at least one tomographic image selected from the tomographic image group is binarized by the center extracting unit, and the center of the cross section of the aorta is extracted by a method such as distance conversion processing, and main region extraction is performed. By performing region expansion processing using the center as an initial region, a main region that includes the aorta and other regions such as branch blood vessels branching from the aorta and bones and organs is extracted.

  Then, the main region is contracted to separate the core region of the aorta from other regions such as branch vessels, bones, and organs. Then, the region including the core region extracted by the core extraction unit and the other region is subjected to region expansion processing using the center as the initial region, so that other regions such as branch vessels, bones, and organs are obtained. The region of only the core part of the aorta is extracted. Then, the aorta region (aorta region) is restored by expanding the extracted core region by the expansion processing unit.

  On the other hand, the tumor position setting unit specifies an arbitrary set point on the target tumor on the three-dimensional image, and the tumor region extraction unit gradually performs region expansion processing using the set point as an initial region. An area (affected area) of (or a bleeding part) is extracted.

  Then, the overlapping region detection unit detects an overlapping region that is a portion where the affected region and the main region overlap. Then, the target blood vessel extraction unit can extract a target blood vessel that connects the tumor (or bleeding portion) and the aorta by performing region expansion processing on the three-dimensional image using the overlapping region as an initial region.

  According to the present invention, for example, by processing a tomographic three-dimensional image of a subject imaged by CT or MRI, a blood vessel connecting an aorta and a tumor, a bleeding part, or the like can be easily specified.

FIG. 1 is a block diagram illustrating a device configuration of a medical image diagnostic system including an image processing device 100 according to the first embodiment. FIG. 2 is an explanatory diagram illustrating an image processing unit of the image processing apparatus 100. FIG. 3 is a flowchart showing a series of processes of the image processing method according to the first embodiment. FIG. 4 is a schematic diagram illustrating processing in each step of the image processing method according to the first embodiment. FIG. 5 shows a flowchart of the area expansion process. FIG. 6 shows a flowchart of the history-added area expansion process. FIG. 7 is an explanatory diagram for explaining the area expansion process with history in two dimensions. FIG. 8 is a diagram showing a state in which the history-added region expansion process is repeated a plurality of times from FIG. FIG. 9 is a diagram illustrating a state in which adjacent volume pixels are examined for the volume pixel c1. FIG. 10 is a diagram illustrating a state in which newly expanded volume pixels d1 to d4 are obtained. FIG. 11 is a diagram illustrating a state in which adjacent volume pixels are examined for the volume pixel c2. FIG. 12 is a diagram illustrating a state where a path is obtained. FIG. 13 is an explanatory diagram for explaining an image obtained by the image processing apparatus according to the first embodiment. FIG. 14 is an explanatory diagram illustrating an image processing unit of the image processing apparatus 200. FIG. 15 is a flowchart showing a series of processes of the image processing method according to the second embodiment. FIG. 16 is a schematic diagram illustrating processing in each step of the image processing method according to the second embodiment.

[First embodiment]
Hereinafter, a first embodiment of an image processing apparatus, an image processing method, and a computer program according to the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating a device configuration of a medical image diagnostic system including the image processing device 100 according to the first embodiment. FIG. 2 is an explanatory diagram illustrating an image processing unit of the image processing device 100. FIG. 3 is a flowchart showing a series of processing steps of the image processing method, and FIG. 4 is a schematic diagram illustrating processing in each step of the image processing method.

  Referring to FIG. 1, a medical image diagnostic system 1000 includes an image processing apparatus (denoted as “IPE”) 100 and a medical image diagnostic apparatus (denoted as “Dia” in FIG. 1) 160. In addition, a medical image storage device (indicated as “IS” in FIG. 1) 150 that stores images taken by the medical image diagnostic device 160 is connected via a wireless network or a wired network.

  The medical image diagnostic apparatus 160 generates a tomographic image group including a plurality of tomographic images by tomographically imaging a target region including a tumor and an aorta of a subject. Specific examples of the medical image diagnostic apparatus 160 include, for example, an X-ray CT (Computed Tomography) apparatus, an MRI (Magnetic Resonance Imaging) apparatus, a PET (Positron Emission Computed Tomography) apparatus, or a combination of these.

  The medical image storage device 150 is a device that receives and stores the tomographic image group captured by the medical image diagnostic device 160 from the medical image diagnostic device 160. When transmitting the tomographic image group to the medical image storage apparatus 150, the medical image diagnostic apparatus 160 may transmit, for example, an identifier (ID) that identifies a patient, apparatus, examination type, series, and the like. . Such an ID or the like is used for a search for acquiring a tomographic image group necessary for the user. In addition, the medical image storage device 150 may be integrated into a computer when a workstation type computer capable of storing a large-capacity image is used as the image processing device 100.

  As shown in FIG. 1, the image processing apparatus 100 is realized by installing, for example, an image processing program (denoted as “Prog” in FIG. 1) 71 for performing image processing of a three-dimensional image in a general-purpose computer. The As a standard configuration, the computer has an arithmetic control unit (indicated as “Acu” in FIG. 1) 50 that is a processor such as a CPU, and a main storage unit (indicated as “MM” in FIG. 1) such as a ROM and a RAM. 60), an auxiliary storage unit such as HDD or SSD 70 (denoted as "EM" in FIG. 1) 70, and an input from a keyboard or mouse for the user to give necessary instructions and inputs from the real space Unit (denoted as “Input” in FIG. 1) 80 and an image display unit (denoted as “Dsp” in FIG. 1) which is a display device such as a CRT display, a liquid crystal display, an organic EL display, a plasma display or the like. 90.

  The auxiliary storage unit 70 stores an image processing program 71 for performing each image processing described later. The arithmetic control unit 50 reads the image processing program 71 from the auxiliary storage unit 70 and loads it into the main storage unit 60 when the computer is activated.

The image processing apparatus 100 executes a series of image processing processes defined in the image processing program 71 installed in the computer. The image processing program 71 is a process to be executed by a computer.
An image forming step (step s1) for generating three-dimensional image data in which a group of tomographic images composed of a plurality of tomographic images obtained by tomographic imaging of a target region including a tumor and an aorta of a subject;
A center extraction step (step s2) for extracting the central pixel of the cross section of the aorta from at least one tomographic image selected from the tomographic image group;
A main region for extracting the main region including the aorta and other regions such as branch vessels and bones and organs branching from the aorta by performing region expansion processing on the 3D image data with the central pixel as an initial region An extraction process (step s3);
A contraction process step (step s4) for separating the main region of the aorta from the other part by contracting the extracted main region;
A core extraction step (step s5) for extracting only the core area by performing area expansion processing on the separated area including the core area and other areas as an initial area. ,
An expansion process step (step s6) for restoring the area of the aorta by expanding the area of the core part;
A tumor position setting step (step s7) for setting an arbitrary point on the tumor in the three-dimensional image data instructed by the user as a set point;
A tumor region extraction step (step s8) for extracting a region of the tumor by performing region expansion processing on the three-dimensional image data using the set point as an initial region;
An overlapping area detecting step (step s9) for detecting an area where the extracted tumor area and the main area overlap;
A target blood vessel extraction step (step s10) for extracting a region of a target blood vessel that connects the tumor and the aorta is defined by subjecting the three-dimensional image data to region expansion processing using the overlap region as an initial region.

  When the image processing program 71 is installed in the computer, the image forming unit 101, the center extraction unit 102, the main region extraction unit 103, the contraction processing unit 104, the core extraction unit 105, the expansion processing unit 106, and the tumor are installed in the computer. A position setting unit 107, a tumor region extraction unit 108, an overlap region detection unit 109, and a target blood vessel extraction unit 110 are configured. FIG. 2 shows a state in which each of these units is configured in the computer to become the image processing apparatus 100.

  The image processing program 71 can be recorded on a recording medium. The type of recording medium is not particularly limited.

  The sequence of image processing performed using such an image processing apparatus 100 will be described with reference to FIG.

(Acquisition of tomographic image group for forming 3D image data of subject (s0))
First, tomographic imaging of a target region including a tumor and an aorta of a subject is performed by the medical image diagnostic apparatus 160. A tomographic image is reconstructed from the obtained projection data and data such as an MR signal, and is stored in the medical image storage device 150 as a tomographic image group (step s0).

  One tomographic image group is composed of n tomographic images. The tomographic image taken at the kth is denoted as Lk, and one tomographic image group is denoted as Set {Ln}. Note that tomographic imaging is performed for each unit distance of one pixel of almost one tomographic image.

  The subsequent series of image processing is executed by the image processing apparatus 100 which is a computer in which an image processing program 71 capable of executing a series of image processing processes is installed. The user of the image processing apparatus 100 selects the ID of the target tomographic image group Set {Ln} from the plurality of tomographic image groups Set {Ln} stored in the medical image storage apparatus 150 by an operation of selecting or inputting the ID. And stored in the auxiliary storage unit 70 of the image processing apparatus 100.

  Then, when executing the image processing, the tomographic image group Set {Ln} to be processed is stored in the main storage unit 60. The tomographic image group Set {Ln} stored in the main storage unit 60 is subjected to the following series of processes. In FIG. 3, “Set {Ln} acquisition” is described in step s0.

(Image forming step (s1))
In this process, three-dimensional image data is generated from the tomographic image group Set {Ln} of the target region including the tumor and the aorta of the subject obtained in step s0. Specifically, the image forming unit 101 generates volume data of a three-dimensional image by stacking a tomographic image group Set {Ln} including a plurality of tomographic images along the body axis direction.

  As described above, tomographic imaging is performed for each distance of about the unit length of one pixel of one tomographic image. Accordingly, one pixel of the tomographic image may be regarded as a volume pixel in the stacking direction of the tomographic image. This indicates that the luminance (pixel value) of one pixel can be regarded as the luminance (pixel value) of one volume pixel.

  That is, one volume pixel can be specified by assigning x coordinates and y coordinates to each pixel of the same tomographic image of the tomographic image group Set {Ln} and assigning a different z coordinate for each tomographic image. If the luminance of one pixel of the tomographic image is assigned as the value of the volume pixel, the tomographic image group Set {Ln} can be handled as three-dimensional image data. Thus, the three-dimensional image data in which the three-dimensional coordinates are associated with the luminance for each coordinate is the volume data.

  The volume data is represented as Vol {Ln} and is also called “all areas”. In FIG. 3, “Vol {Ln} generation” is described in step s1. More specifically, if there are A × B pixels in one tomographic image, the entire region Vol {Ln} includes A × B × n coordinates, and the volume pixel of each coordinate is And a set of data having pixel values of the tomographic image group Set {Ln}. Note that data other than the pixel value may be included in each volume pixel of the entire region Vol {Ln}.

  By displaying the entire region Vol {Ln} in three dimensions, a three-dimensional image including the tumor and aorta of the subject as shown in FIG. 4A can be obtained. 4A, reference numeral 1 is an aorta, reference numeral 2 is a branch vessel branched from the aorta, reference numeral 3 is an organ not including a tumor, reference numeral 4 is an organ including a tumor 8, reference numeral 5 is a rib, reference numeral Reference numeral 6 denotes an area indicating the spine.

(Center extraction step (s2))
Refer to FIG. 3 again. In this process, the central pixel P1 of the cross section of the aorta is extracted based on the tomographic image of at least one layer selected from the three-dimensional image data Vol {Ln} obtained in step s1. The center extraction unit 102 extracts the central pixel P1 of the cross section of the aorta based on at least one layer of the tomographic image selected from the three-dimensional image data Vol {Ln}. Reference numeral 10 in FIG. 4B indicates a one-layer tomographic image 10 in which the central pixel P1 of the aorta constituting the three-dimensional image data Vol {Ln} is specified.

  In FIG. 4B, the tomographic image 10 is illustrated as the first tomographic image L1. Tomographic images L2, L3,... Are stacked below the tomographic image L1. The stacking direction of each tomographic image is the body axis direction. In FIG. 4B, the planar images such as L1 and L2 are shown as being arranged, but the thickness between the layers is the thickness of the volume pixel in the z-axis direction, which corresponds to one side of one pixel of the tomographic image. Is the distance.

  As the one-layer tomographic image for extracting the center pixel P1, for example, the one layer on the most head side is selected or selected by the user. For example, the center extraction unit 102 binarizes the selected tomographic image, and extracts the center pixel P1 of the cross section of the aorta by distance conversion processing.

  The distance conversion process is, for example, conversion that replaces the pixel value of one pixel of a binary image with the distance from the pixel to the pixel with the closest pixel value of 0. Therefore, before the distance conversion process, the binarization process is performed on the target tomographic image. The binarization process is a process of comparing a pixel value of all pixels of a target tomographic image with a certain threshold value Vth, setting the pixel value to 1 if it is equal to or greater than the threshold value Vth, and setting the pixel value to zero if it is smaller than the threshold value Vth. It is. This processing may be performed by the center extraction unit 102. In the present embodiment, the distance conversion process is specifically performed as follows. Note that the one-layer tomographic image selected for obtaining the central pixel P1 from the entire region Vol {Ln} is L1. The tomographic image L1 may be said to be a set of data in which the z coordinate of the entire region Vol {Ln} is 1 (Ln = 1).

  First, binary conversion is performed on all volume pixels for the tomographic image L1. A pixel group having a pixel value of 1 adjacent to a pixel having a pixel value of 0 (referred to as D1; see FIG. 4B) is searched. Then, a pixel group (referred to as D2) having a pixel value 1 in a region adjacent to the pixel group D1 is specified. Further, a pixel group (referred to as D3) in an area adjacent to the pixel group D2 is specified.

  Such an operation is repeated for the pixel having the pixel value 1 of the entire image, and the pixel group Dn farthest from the pixel group D1 is obtained. n is the number of pixels from a pixel having a pixel value of 0 adjacent to the pixel group D1, and this is a distance to a pixel having a pixel value of 0. The pixel group Dn with the largest n is a position near the center of the aorta.

  That is, in the tomographic image L1, since the aorta portion is a large structure having higher luminance (pixel value) than the other portions, the region having the longest distance from the boundary line of the aorta can be regarded as the center of the aorta. If the obtained pixel group Dn is one pixel, that pixel is set as the central pixel P1. When the obtained pixel group Dn includes a plurality of pixels, any one is selected as the central pixel P1.

  This method can be obtained one after another for all volume pixels of the tomographic image L1. Therefore, it is convenient because it does not require user judgment. In FIG. 3, step s2 is described as “P1 extraction”.

  Further, the user may manually designate the vicinity of the center of the aorta with respect to the tomographic image L1. The central pixel P1 may be a starting point for the subsequent process of extracting the aorta. Therefore, it is not necessary to be an accurate center pixel in the aorta portion on the tomographic image L1. That is, the center extraction process may not use the distance conversion process.

(Main area extraction step (s3))
Please refer to FIG. 3 again. In this process, the central pixel P1 of the tomographic image 10 obtained in the center extraction step (step s2) is used as an initial region (starting point), and region expansion processing is performed on the volume pixels of the entire region Vol {Ln}, thereby And a main region Main {Ln} including branch blood vessels branching from the aorta and other regions such as bones and organs is extracted. The main region Main {Ln} includes at least the aorta, and includes other regions that are in contact with the aorta and are difficult to separate from the aorta because the pixel values are close.

  A schematic diagram of the extracted main region Main {Ln} is shown in FIG. In FIG. 4C, the hatched portion is the main region Main {Ln}. In FIG. 4C, reference numeral 1 is an aorta, reference numeral 2 is a branch vessel branched from the aorta, reference numeral 3 is an organ not including a tumor, reference numeral 4 is an organ including a tumor 8, reference numeral 5 is a rib, reference numeral 6 is a spine. It is an area shown. Note that the ribs 5 that are not hatched are not included in the main region Main {Ln}. That is, in the entire region Vol {Ln} in this example, there is no volume pixel that connects the rib 5 and the aorta 1 that are not hatched.

  In the extraction of the main region Main {Ln}, the region expansion process is executed on the entire region Vol {Ln} with the center pixel P1 of the tomographic image 10 obtained in the center extraction step (step s2) as an initial region. In the region expansion process, a main region Main {Ln} including other regions such as a branch blood vessel branched from the aorta and the aorta and bones and organs is extracted from the entire region Vol {Ln}.

  Further, the main region extraction unit 103 sets the pixel of the center pixel P1 as an initial region, and performs region expansion processing to thereby contact the aorta and the aorta from the volume data (all regions Vol {Ln}) of the three-dimensional image, and Other regions that are difficult to clearly separate from the aorta because the pixel values are close are extracted.

  A specific processing example of the region expansion processing is based on the flow shown in FIG. Referring to FIG. 5, when the process starts (step S100), the volume pixel Pst at the start point of the area expansion process is set as the volume pixel Px (step S102). Here, Pst is the center pixel P1, and the coordinate data and pixel value of the center pixel P1 are copied to the pixel Px.

  Next, the pixel values of the 26 volume pixels PPx around the volume pixel Px are compared with the threshold value VPth (step S104). The volume pixel PPx indicates a surrounding volume pixel when the volume pixel Px is placed at the center of a collection of 3 × 3 volume pixels. In FIG. 5, the pixel value of the volume pixel PPx is represented as “| PPx |”. If the pixel value of the volume pixel PPx is equal to or greater than the threshold value VPth (Y branch in step S104), the volume pixel PPx is included in the main region Main {Ln} (step S106).

  Here, “include” means that all pixel values are set to zero when the main area Main {Ln} is first allocated on the main memory, and the pixel value is set to the volume pixel on the same coordinate as the volume pixel PPx. | PPx | may be substituted.

  If the pixel value of the volume pixel PPx is smaller than the threshold value VPth (N branch in step S104), it is not included in the main region Main {Ln}. Alternatively, the pixel value may be zero. This is performed for all PPx (step S108). Although FIG. 5 briefly describes steps S104 to S108, the process during this time is performed for all 26 volume pixels PPx around the volume pixel Px. That is, step S104 and step S108 are repeated 26 times.

  Next, it is determined whether or not 26 surrounding volume pixels have been examined for all the volume pixels belonging to the main region Main {Ln} (step S110). If there is a volume pixel that has not been examined yet, the volume pixel is set as a new Px (step S112), and the process returns to step S104. When the pixel values of the surrounding 26 volume pixels are compared with the threshold value VPth for all the volume pixels of the main region Main {Ln} (Y branch in step S110), the pixel values of the volume pixels of other coordinates are set to zero ( Step S114), the flow ends (Step S116).

  Through the above processing, a volume pixel having a pixel value equal to or higher than VPth and connected to the central pixel P1 in the entire region Vol {Ln} can be extracted as the main region Main {Ln}. Here, the outermost volume pixel of the entire region Vol {Ln} does not have 26 volume pixels around it. However, the pixel value of the non-existing volume pixel may be set to zero. In FIG. 3, step s3 is described as “Extraction of Main {Ln}”.

  Note that the center extraction step of step s2 may be included in this main region extraction step. This is because the center pixel P1 is necessary to execute the main region extraction step (step s3). Further, as the image processing apparatus 100, the central portion extraction unit 102 may be included in the main region extraction unit 103.

  Here, the selection of the volume pixel is performed under the condition that the pixel value of the volume pixel is equal to or greater than the threshold value VPth. That is, if the condition that the pixel value of the volume pixel is equal to or greater than the threshold value VPth and the absolute value of the difference between the pixel value of PPx and the pixel value of Px is within the threshold value VPth2, the volume pixel is included in the main region Main {Ln}. You may include the condition. By doing so, only pixels having similar pixel values, that is, pixels having pixel values close to blood vessels can be expanded.

(Shrinkage treatment step (s4))
Refer to FIG. 3 again. In this process, the main region Main {Ln} extracted in the main region extraction step (s3) is subjected to contraction processing by the contraction processing unit 104, thereby separating the core portion of the aorta from other regions. That is, by contracting the image of the main region Main {Ln}, the core region of the aorta is separated from other regions such as narrow blood vessels such as branch blood vessels, bones such as ribs and spine, and organs. To do.

  FIG. 4D shows a schematic diagram of a three-dimensional image including the core 1a formed in this way, an organ 3 that does not include a tumor, and an organ 4 that includes a tumor 8. FIG. A region obtained by contracting the main region Main {Ln} is defined as a contracted main region SMain {Ln}.

  In the contraction processing as described above, the image data of the main region Main {Ln} is contracted to erase most or all of the regions such as branch vessels, bones and organs, and mainly the core region of the aorta It is processing that leaves.

  More specifically, the pixel value of the volume pixel at the boundary of the main region Main {Ln} adjacent to the region that is not the main region Main {Ln} including the aorta is set to a pixel value that is not the main region Main {Ln} (even if it is zero). Is formed into a new main region Main {Ln} (contracted main region SMain {Ln}).

  Then, the pixel value of the volume pixel at the boundary of the portion that has become the contracted main area SMain {Ln} is converted into a pixel value that is not the contracted main area Main {Ln}. By repeating such an operation a plurality of times, the thin blood vessel portion becomes the same as the pixel value of the region other than the main region SMain {Ln} in which the pixel value is contracted. That is, the thin blood vessels connected to the region of the aorta are erased one after another. The contraction process is repeated a predetermined number of times (this is F times). It is desirable to perform the contraction processing frequency F so that all blood vessels connected to the aorta 1 disappear.

  In this manner, the core region of the aorta can be separated from other regions by contracting the main region Main {Ln}. The contracted main region SMain {Ln} includes the region of the core of the aorta 1 and the region remaining in the contraction process in the main region Main {Ln}. Specifically, it is a large organ connected to the aorta. In FIG. 3, step s4 is described as “determining SMain {Ln}”.

(Core extraction step (s5))
In this process, only the core portion of the aorta 1 is extracted from the contracted main region SMain {Ln} separated in the contraction processing step (s4). The contracted main region SMain {Ln} includes the core of the aorta 1 and the organ 3 remaining in the contraction processing step (s4) (a region of a volume pixel having a pixel value equal to or greater than the threshold value VPth). Therefore, only the core 1b of the aorta 1 is extracted by performing region expansion processing using the center pixel P1 extracted in the center extraction step (s2) as an initial region. The area data thus obtained is defined as a core area CMain {Ln}.

  Between the core of the aorta 1 and the other organs 3 and the like, there are no volume pixels (volume pixels having a pixel value equal to or greater than the threshold value VPth) corresponding to blood vessels to be connected. Therefore, when the region expansion process with the central pixel P1 as an initial value is performed on the contracted main region SMain {Ln}, the region does not expand from the core of the aorta 1 to other organs, and the core of the aorta Only 1b is extracted. In other words, in this core extraction process, the area other than the core is erased from the contracted main area SMain {Ln} (the pixel value is zero) (core area CMain {Ln}). It may be said that only is extracted. FIG. 4E shows the core region CMain {Ln} including only the core 1b generated in this way.

  The core extraction unit 105 performs region expansion processing using the central pixel P1 as an initial region on the contracted main region SMain {Ln} generated in the contraction processing step (step s4). The specific processing flow is the same as the flow shown in FIG. By this processing, branch blood vessels, bones, organs, and the like are deleted, and only the core 1b of the aorta 1 (core region CMain {Ln}) is extracted. In FIG. 3, step s5 is described as “determining CMain {Ln}”.

(Expansion treatment step (s6))
In this process, the core region CMain {Ln} extracted in the core extraction step (s5) is expanded to extract the outline of the aorta and restore the outline of the aorta. The volume pixel representing the restored aorta is referred to as “restored main region RMain {Ln}”. FIG. 4F shows the region of the aorta 1 of the subject thus generated (reconstructed main region RMain {Ln}).

  The expansion processing is executed by the expansion processing unit 106 as follows, for example. Contrary to the contraction processing, the pixel value of the volume pixel of the area that is not the core area CMain {Ln} adjacent to the core area CMain {Ln} is the pixel value of the volume pixel of the adjacent core area CMain {Ln}. Replace with a value. That is, the core area CMain {Ln} is thickened by one volume pixel. That is, the volume pixel whose pixel value is greater than or equal to the threshold VPth increases. In this way, the restored main region RMain {Ln} is obtained.

  Then, the pixel value of the volume pixel of the region that is not adjacent to the restored main region RMain {Ln} and that is newly adjacent to the restored main region RMain {Ln} is further restored to that of the restored main region RMain {Ln}. Replace with pixel value. By repeating such an operation a plurality of times and expanding the core region CMain {Ln}, the outline of only the aorta 1 (the restored main region RMain {Ln}) is restored.

  When the expansion process is performed on the main area RMain {Ln} restored from the core area CMain {Ln}, volume pixels that are not included in the main area Main {Ln} (that is, the pixel value is zero) are restored. It is desirable not to be included in the main area RMain {Ln}. The expansion process may be performed the number of times (F times) of the contraction process performed in the contraction process (step s4). This is because the aortic region is considered to be almost restored. Of course, the number may be other than F times. In FIG. 3, step s6 is described as “finding RMain {Ln}”.

  Reconstructed main region RMain {Ln} showing only the aorta (the pixel value of the volume pixel of the other part is zero) by the contraction processing step of step s4, the core extraction step of step s5, and the expansion processing step of step s6 Was requested. The restored main region RMain {Ln} may be referred to as an “aortic region”. Therefore, these three steps may be called “aortic region extraction step”. Similarly, in the image processing apparatus 100, the contraction processing unit 104, the core extraction unit 105, and the expansion processing unit 106 constitute an aorta region extraction unit.

(Tumor position setting step (s7))
In this process, the tumor position setting unit 107 sets an arbitrary set point P2 on the tumor in the entire region Vol {Ln} formed by the image forming unit 101. The position of the set point P2 may be set by the tomographic image set Set {Ln} taken by the medical image diagnostic apparatus 160. In any case, in the present process, the coordinates of the volume pixel at the location determined to be a tumor in the entire region Vol {Ln} are designated. The setting point P2 may be automatically specified by software by image processing, or the user may specify it directly while looking at the tomographic image group Set {Ln}.

  As a specific procedure, in the entire region Vol {Ln} that is three-dimensional image data, a cross-sectional image that is considered to show a tumor is displayed on the image display unit 90 as a two-dimensional image. The cross-sectional image is data having the same z coordinate in the entire region Vol {Ln}. This data includes four components: an x coordinate, a y coordinate, a z coordinate, and a pixel value. The user designates an arbitrary point on the target tumor on the image display unit 90 as the set point P2 by operating a pointing device such as a mouse as the input unit 80 while checking the image display unit 90. The set point P2 is an affected part volume pixel.

  The position of the set point P2 is not particularly limited as long as it is a point on the tumor. However, tumors are often spherical, and the area expansion process performed in the next process expands the area in all directions. Therefore, in order to efficiently extract the tumor region, it is particularly preferable to set the point near the center of the tumor as the set point P2.

  The tumor position setting unit 107 identifies an arbitrary position on the tumor designated by the user, and sets that point as the set point P2. A region in the vicinity of the tumor of the subject representing the set point P2 set in this way is shown in FIG. FIG. 4G shows a state where the set point P2 is designated near the center of the tumor 8 in the organ 4. In FIG. 3, step s7 is described as “setting P2.”

(Tumor region extraction step (s8))
In this process, the tumor region extraction unit 108 sets the set point P2 set in the tumor position setting step (s7) to the volume data Vol {Ln} of the three-dimensional image generated in the image forming step (s1). The tumor region C {Ln} is determined by repeating the region expansion process as an initial region a predetermined number of times. The number of repetitions of the region expansion process needs to be the number of times until the tumor region C {Ln} includes blood vessels around the tumor. In FIG. 3, step s8 is described as “determining C {Ln}”. The tumor area C {Ln} may be referred to as an affected area C {Ln}. The specific processing flow of the area expansion processing is the same as the flow shown in FIG. That is, the tumor region C {Ln} is data in which the pixel value of each volume pixel is zero when assigned to the main memory.

  Further, the user may specify a range for expanding the region from the set point P2 set in the tumor position setting step (s7). Specifically, after the set point P2 is set, a radius r to be expanded from the set point P2 is input by a user instruction. A specific numerical value may be input from the input unit 80. Further, by operating a pointing device such as a mouse on the image display unit 90, the user may further specify a range to be expanded.

  After the set point P2 and the radius to be expanded are determined, among the volume pixels included in the spherical region having the radius r centered on the set point P2, a volume pixel having a pixel value equal to or greater than the threshold VPthC is selected as the tumor region C {Ln}. Include in Including here means that the pixel value of the corresponding volume pixel is the pixel value of the corresponding volume pixel of the tumor region C {Ln}. This process does not expand the area for each volume pixel such as the area expansion process or the expansion process, but includes the volume pixels of the threshold VPthC included in the designated spherical area in the tumor area C {Ln}. Measure time savings. Moreover, when there are a plurality of tumors within a certain range, there is an effect that they can be collectively processed as a tumor region C {Ln}.

  Further, the region expansion process may be performed a predetermined number of times on the tumor region C {Ln} obtained in this way.

  Further, the tumor position setting step in step s7 and the tumor region extraction step in step s8 may be referred to as “affected area extraction step”. In the image processing apparatus 100, the tumor position setting unit 107 and the tumor region extraction unit 108 constitute an “affected region extraction unit”. Further, the input part 80 may be included in the affected area extraction unit.

(Overlapping area detection step (s9))
In this process, the overlapping region detection unit 109 detects the tumor region (affected region) C {Ln} representing the tumor 8 identified in the tumor region extraction step (s8) and the main region extracted in the main region extraction step (s3). An overlapping area L {Ln} with the area Main {Ln} is detected. By this process, the origin of the blood vessel connected to the aorta 1 of the identified tumor 8 can be extracted. In FIG. 4G, L {Ln} is an overlapping region L {Ln} including a blood vessel connected to the aorta 1 extending from the tumor 8. Specifically, it is only necessary to find a volume pixel whose pixel value common to each region is VPth or more or whose pixel value is VPthC or more.

  Note that there may be a plurality of overlapping regions L {Ln}. This is because the tumor 8 sometimes obtains blood from a plurality of blood vessels. In step s9 of FIG. 3, “determine overlapping region L {Ln}” is described.

(Target blood vessel extraction step (s10))
In this process, the target blood vessel extraction unit 110 uses the overlap region L {Ln} detected in the overlap region detection step (s9) as an initial region, and the volume data Vol of the three-dimensional image generated in the image formation step (s1). For {Ln}, a region expansion process with history is performed so as to extract a blood vessel that reaches the aorta 1 from the initial region. Further, a target blood vessel may be extracted from the main region Main {Ln} obtained in the main region extraction step (step s3). Here, the description is continued for the main area Main {Ln}.

  In the area expansion process with history, pixels belonging to the main area Main {Ln} are taken into the work area W {Ln} by one volume pixel from the initial area, and the area is expanded. At that time, it is recorded which volume pixel the volume pixel newly belonging to the work area W {Ln} has spread. Then, a path that leads from the initial region provided in the tumor to the restored main region RMain {Ln} is found.

  FIG. 7 shows a schematic diagram of a two-dimensional region extension process with history in order to explain the region extension process with history, and FIG. 6 shows a process flow. Please refer to FIG. 5 × 8 squares are shown. Each square is a pixel. Although it is actually a volume pixel, a plane pixel is used here for explanation. A white cell is a part of the main region Main {Ln}. A black square is an area other than the main area Main {Ln}, and indicates that the pixel value is zero. The pixel value of the volume pixel belonging to the main region Main {Ln} is equal to or greater than the threshold value VPth.

  The starting point volume pixel St1 belonging to the overlapping region L {Ln} is the starting point of the region expansion process with history. That is, the volume pixel St1 is a volume pixel of the tumor region C {Ln}. The volume pixel PPcm is a volume pixel belonging to the restored main region RMain {Ln}. That is, the volume pixel PPcm is a volume pixel of the aorta 1. The white cells other than the volume pixels St1 and PPcm are blood vessels in which the main region Main {Ln} exists between the tumor region C {Ln} and the restored main region RMain {Ln}.

  Reference is also made to FIG. When the process starts (step S200), initialization is performed (step S202). In the initial setting, the start point volume pixel St1 is included in the work area W {Ln}. Here, “include” means that the pixel value of the start point volume pixel St1 is substituted into the pixel value of the corresponding volume pixel in the work area W {Ln}. Also, St1 is substituted into the processing target PPci (indicated as PPc1 because it is the first in the figure). The work area W {Ln} is a storage area in which expanded volume pixels are collected as a result of the area expansion process with history.

  Therefore, the volume pixel belonging to the work area W {Ln} has history information indicating from which volume pixel the pixel is expanded. The history information may be called an extended history. However, since the start point volume pixel St1 is the first start point, it does not have history information. In FIG. 6, since there is only one processing target PPci (St1), PPc1 = St1.

  The processing target PPci is a volume pixel that is a source for performing the area expansion process with history. The area expansion process with history determines whether or not to include a volume pixel adjacent to the processing target PPci in the work area W {Ln}. The number of volume pixels PPci increases as the process proceeds. The subscript i represents the number.

  In step S204, the processing target PPci is substituted into the volume pixel variable Pxk. In FIG. 7, the coordinate data, pixel value, and history information of the processing target PPc1 are substituted into the volume pixel variable Px1. In the following, the volume pixel variable Pxk is generated for the number of processing target PPci, for PPc2 data to Px2 and PPc3 data to Px3. This is to prepare for the area expansion process with history. In FIG. 7, only Px1 = St1.

  In step S206, all data of the processing target PPci is cleared. This is because the data of the newly expanded volume pixel can be substituted in a later step.

  In step S208, the pixel values of the surrounding 26 volume pixels PPxkl and the presence / absence of history information are checked for all volume pixel variables Pxk. There are 26 volume pixels PPxkl for one volume pixel variable Pxk. That is, from PPxk1 to PPxk26. Then, history information that is expanded from the volume pixel Pxk is added to the volume pixel variable PPxkl that is included in the main region Main {Ln} and does not have history information, and is substituted into PPci.

  In FIG. 6, the volume pixel PPxkl that is included in the main region Main {Ln} and satisfies the condition of having no history information is represented as “OK [PPxkl]”. “+ Α” indicates that history information has been added. The processing target PPci is assigned a number each time it is generated, such as PPc1, PPc2, PPc3,. When the inspection of the surrounding 26 volume pixels is completed for all volume pixel variables Pxk, this step is ended.

  In FIG. 7, among the pixels around Px1 (= St1), volume pixels a1, a2, and a3 that belong to the main region Main {Ln} and have no history information correspond to expanded volume pixels. Since the volume pixels a1, a2, and a3 are all expanded from the volume pixel St1, the history information of these pixels is all St1. The three volume pixels are substituted into PPci as much as possible for a new processing target (step 208). Specifically, PPc1 = a1, PPc2 = a2, and PPc3 = a3.

  Next, in step S210, all the processing targets PPci obtained in step S208 are stored in the work area W {Ln}. The processing target PPci is a volume pixel newly expanded from the volume pixel variable Pxk, belongs to the main region Main {Ln} including an organ connected to the aorta, and has history information.

  In step S212, it is determined whether any of the newly expanded processing target PPci belongs to the restored main area RMain {Ln}. That is, it is determined whether or not the expanded volume pixel PPci has reached the aorta. That is, it is determined whether or not the volume pixels a1, a2, and a3 shown in FIG. 7 are aortic volume pixels. In FIG. 7, it is determined whether or not there is PPcm among the volume pixels a1 to a3. In FIG. 7, it is determined that it has not reached.

  If this determination is No, the process returns to step S204, and the above processing is continued with the expanded processing target PPci as the new start volume pixel variable Pxk (N branch of step S212).

  If this determination is Yes, it means that a path from the start point volume pixel St1, which is the first start point, to the aorta 1 has been found. In FIG. 7, it is found that the one corresponding to the volume pixel PPcm belonging to the restored main region RMain {Ln} is found among the volume pixels PPci. This state is shown in FIG.

  Next, a specific example of a state in which the above process is repeated will be described. FIG. 8 shows a state in which step 204 to step 212 are repeated three times. The volume pixels b1, b2, b3, and b4 are volume pixels that are expanded from the volume pixel a1. Volume pixels c1, c2, c3, and c4 are volume pixels that are expanded from the volume pixel b1. If the processing target PPci at this point is clearly described, PPc1 = c1, PPc2 = c2, PPc3 = c3, PPc4 = c4.

  Referring also to FIG. 6, when returning to step S204, the processing target PPci is substituted into the volume pixel variable Pxk. Therefore, Px1 = c1, Px2 = c2, Px3 = c3, and Px4 = c4. In step S206, PPc1 to PPc4 are cleared.

  Proceeding to step S208, surrounding volume pixels PPxkl are sequentially examined for all volume pixel variables Pxk. FIG. 9 shows a state in which adjacent peripheral pixels are examined for the pixel Px1 (= c1). For the volume pixel c1, PPx11 to PPx18 volume pixels are the objects to be examined. “PPx11” means the first volume pixel around the volume pixel c1. The third volume pixel around the volume pixel c2 is “PPx23”. The order of the numbers of the surrounding volume pixels may be arbitrary. Here, since it is only two-dimensional, it is eight volume pixels, but actually 26 volume pixels are examined.

  Among the PPx11 to PPx18, four volume pixels PPx11, PPx12, PPx13, and PPx18 correspond to the volume pixel PPxkl that is included in the main region Main {Ln} and has no history information. Therefore, these four volume pixels are input to the (new) processing target PPci together with the history information that the expansion source is the volume pixel c1. When these four pixels are d1 to d4 and the processing target PPci is clearly described, PPc1 = d1, PPc2 = d2, PPc3 = d3, and PPc4 = d4. FIG. 10 shows this state.

  In this way, other volume pixel variables Pxk (c2, c3, c4) are also examined. However, here, all of the surroundings of these volume pixels are not included in the main area Main {Ln}, or are volume pixels to which history information has already been assigned. For example, FIG. 11 shows a pixel PPxkl around the volume pixel c2 (= Px2). Specifically, PPx21 to PPx28. All of these eight volume pixels have history information already created or do not belong to the main area Main {Ln}. Accordingly, the volume pixels newly expanded in this processing are four volume pixels d1, d2, d3, and d4, and their history information is c1.

  Referring to FIG. 6 again, in step S214, a specific path is obtained. The presence of the volume pixel PPcm in the expanded volume pixel PPci means that history information is added to the volume pixel PPcm. From this history information, the adjacent expansion source volume pixel is known. Since the expansion source volume pixel is recorded in the work area W {Ln}, the history information of the volume pixel is also known. In this way, the arrangement of volume pixels from the volume pixel PPcm to the start point volume pixel St1 can be obtained. This is the path. These volume pixels are defined as a path P {PSk}. PS1 is the start point volume pixel St1, and PSn is the volume pixel PPcm. It is assumed that there are n PSk.

  FIG. 12 shows a specific example of the path. When the history-added area expansion process is further performed from FIG. 10, FIG. 12 is obtained. When the volume pixel variable Pxk is f1 to f4, the expanded volume pixel (processing target) PPci indicated by g1 to g3 is obtained as a result of performing the region expansion process with history (step S208). Then, one of the volume pixels g1 coincides with PPcm belonging to the restored main region (aorta region) RMain {Ln} representing only the aorta 1 (step S212). Accordingly, the history information is traced from the volume pixel PPcm (g1), and a path to the start point volume pixel St1 is obtained. In FIG. 12, St1, a1, b1, c1, d1, e1, f1, and PPcm (g1) are volume pixels belonging to the path P {PSk}.

  Referring to FIG. 6, in step S216, expansion processing is performed for all volume pixels of path P {PSk} until a point not included in main region Main {Ln} is found. Further, when the region expansion process proceeds to the restored main region RMain {Ln} and tumor region C {Ln}, no further region expansion processing is performed. Further, the area expansion process may be performed a predetermined number of times. This is because the area expansion process does not select a volume pixel whose pixel value is smaller than VPth, and therefore does not become thicker than the target blood vessel area is imaged. The pixel value of the volume pixel whose area has been expanded is recorded as the pixel value of the volume pixel corresponding to the target blood vessel area Ob {Ln}. In other words, the target vascular region Ob {Ln} is obtained by subjecting the volume pixels along the expansion history from the affected region C {Ln} to the aortic region RMain {Ln} to region expansion processing.

  Through the above processing, the target blood vessel region Ob {Ln} representing only the target blood vessel connecting the tumor 8 and the aorta 1 can be extracted. The aorta 1 (reconstructed main region RMain {Ln}) and tumor 8 (tumor region C {Ln}) of the subject extracted in this manner and the region of the blood vessel 20 connecting them (target blood vessel region Ob {Ln}) The extracted image including) is shown in FIG. Here, only one target blood vessel 20 is shown, but there may be a plurality of target blood vessels 20. In FIG. 3, step s10 is described as “determining the target blood vessel region Ob {Ln}”.

  Note that the overlapping region detection step in step s9 may be included in the target blood vessel extraction step in step s10. In the image processing apparatus 100, the overlapping region detection unit 109 may be included in the target blood vessel extraction unit 110.

(Image display step (s11))
In this process, the image extracted and generated in the target blood vessel extraction step (s10) is displayed on the image display unit 90 as a three-dimensional image. It is desirable to display at least three data of the restored main region RMain {Ln}, tumor region C {Ln}, and target blood vessel region Ob {Ln}, which are at least the aortic region. In addition, the main area Main {Ln} and the entire area Vol {Ln} may be displayed by superimposing or subtracting these three data. Of course, these data can be displayed by appropriately changing the color or luminance.

  By such processing, it is possible to selectively display a blood vessel 20 that is a target blood vessel connecting the tumor 8 and the aorta 1. The generated three-dimensional image is illustrated in FIG. In addition, in FIG. 13, the figure when the image of the rib 5 and the spine 6 is piled up with the dotted line is shown.

  As shown in FIG. 13, according to the image processing apparatus and the image processing method of the present embodiment, the blood vessel leading to the path from the aorta 1 to the tumor 8 is specified by performing image processing on the tomographic three-dimensional image of the subject. Can be displayed. According to such an image, a blood vessel that reaches the path to the tumor 8 or the like by using a shape feature such as a branching point or a bending point of the aorta 1 or an image of the rib 5 or the spine 6 as a mark. Twenty positions can be specified.

  Heretofore, an example of the image processing apparatus, the image processing method, and the computer program according to the present embodiment has been described in detail. The image processing apparatus according to the present embodiment is not limited to the above example, and various modifications may be added. For example, when there are a plurality of tumors in the three-dimensional image data Vol {Ln} generated from the tomographic image group Set {Ln}, as shown by the broken line in FIG. 3, after the image display step (s11). Furthermore, it is possible to return to the tumor position setting step (s7) and designate another tumor.

  After that, by repeating the steps up to the target blood vessel extraction step (s10) or the image display step (s11) as many times as the number of tumors as described above, target blood vessels connected to a plurality of tumors are obtained. Each can also be extracted.

  Further, in the case of a tumor connected to a plurality of blood vessels, the number of blood vessels is determined by the steps from the overlapping region detection step (s9) to the target blood vessel extraction step (s10) or the image display step (s11) for each blood vessel. The target blood vessels connected to the aorta for each blood vessel may be respectively extracted by repeating the same multiple times.

[Second Embodiment]
The image processing apparatus, the image processing method, and the computer program according to the present embodiment are the first from the acquisition (s0) of the tomographic image group Set {Ln} for forming the three-dimensional image data of the subject to the expansion processing step (s6). It is the same as that of one embodiment. In the first embodiment, in order to specify the origin of the blood vessel extending from the tumor, the user designates the set point P2 to specify the position of the tumor on the known tumor. In the following description, expressions such as “include in area” and “belong to area” are the same as those in the first embodiment.

  Next, the tumor region C {Ln} is extracted by performing region expansion processing from the set point P2, and the overlapping region L {Ln} serving as the blood vessel origin is defined as the tumor region C {Ln} and the main region Main {Ln}. Detected from overlap. Then, the blood vessel 20 that connects the tumor 8 and the aorta 1 was specified by expanding the region with history from the overlap region L {Ln} to the aorta 1 (reconstructed main region RMain {Ln}).

  On the other hand, in the present embodiment, when a region indicating a known blood vessel or bleeding is recognized in advance, such a known volume pixel is designated as a starting point, and the region with history is expanded from the starting point. Thus, an example of extracting a blood vessel connecting the starting point and the aorta 1 will be described. In addition, description is abbreviate | omitted about the process similar to 1st embodiment. Moreover, the element of the same code | symbol as 1st embodiment shows an element similar to 1st embodiment.

  FIG. 14 is an explanatory diagram for explaining the image processing unit of the image processing apparatus 200, FIG. 15 is a flowchart showing a series of processing steps of the image processing method, and FIG. 16 shows the steps of the image processing method. It is the schematic diagram which illustrated the process. The image processing apparatus 200 has a device configuration substantially similar to that of the image processing apparatus 100 shown in FIG.

  Further, instead of the image processing program 71 of the first embodiment, an image processing program 72 that performs image processing by the process shown in the flowchart of FIG. 15 is used as the image processing program. That is, the image processing program 72 of the present embodiment, as the processing to be executed by the computer, is similar to the first embodiment in the image forming process (step s1), the center extracting process (step s2), and the main area extracting process ( Step s3), a contraction process (step s4), a core extraction process (step s5), and an expansion process (step s6) are defined.

  On the other hand, instead of the tumor position setting step (step s7), the tumor region extraction step (step s8), the overlapping region detection step (step s9), and the target blood vessel extraction step (step s10), the tertiary indicated by the user A starting point setting step (step s20) for setting an arbitrary point of a branch blood vessel or a region indicating bleeding in the original image data as a starting point, and region expansion processing with the starting point as an initial region for the three-dimensional image data A target blood vessel extraction step (step s21) for extracting a target blood vessel region connecting the start point and the aorta is defined.

  Then, as shown in FIG. 14, an image processing program for executing the above steps is installed in the computer, so that the image forming unit 101, the center extracting unit 102, the main area extracting unit 103, the contraction processing unit 104, the core A part extraction unit 105, an expansion processing unit 106, a start point setting unit 207, and a target blood vessel extraction unit 210 are configured.

  Image processing executed using such an image processing apparatus 200 will be described along a series of processing flows.

  As described above, the image processing executed using the image processing apparatus 200 is expanded from the acquisition (s0) of the tomographic image group Set {Ln} for forming the three-dimensional image data of the subject shown in FIG. Up to the processing step (determining the restored main region RMain {Ln}) (s6), the processing proceeds as in the first embodiment. As a result, the process proceeds from (a) to (e) shown in FIG. 16 as in the first embodiment. Therefore, the steps after the start point setting step of setting an arbitrary point of the branch blood vessel or bleeding region in the 3D image data instructed by the user as the start point will be described in detail.

(Start point setting step (s20))
In this process, the start point setting unit 207 makes a known point on the blood vessel (P3) in the entire region Vol {Ln} formed by the image forming unit 101, or a point on the region indicating bleeding (P4). ) Is set as the starting point. Further, a point (P3) on the blood vessel or a region (P4) showing bleeding may be set in the tomographic image group Set {Ln} taken by the medical image diagnostic apparatus 160. In any case, in this process, the coordinates of the volume pixel at the location determined as the start point (P3, P4) in the entire area Vol {Ln} are designated. This designation may be made by direct user instruction. The starting point (P3, P4) is the affected area volume pixel.

  As a specific procedure, in the entire region Vol {Ln}, which is three-dimensional image data, a cross-sectional image that is considered to show a portion indicating a known blood vessel or bleeding is displayed on the image display unit 90 as a two-dimensional image. . The cross-sectional image is data having the same z coordinate in the entire region Vol {Ln}. This data includes four components: an x coordinate, a y coordinate, a z coordinate, and a pixel value. The user operates a pointing device such as a mouse as the input unit 80 while confirming the image display unit 90, thereby starting a point on the target blood vessel on the image display unit 90 or a point on the region indicating bleeding. Designate as (P3 or P4).

  The start point setting unit 207 identifies a point designated by the user, and sets the point as a point (P3) on the target blood vessel or a point (P4) on the recognized bleeding region. The points P3 and P4 set in this way are shown in FIG.

(Target blood vessel extraction step (s21))
In this process, the target blood vessel extraction unit 210 uses, as an initial region, a point (P3) on the known target blood vessel set in the start point setting step (s20) or a point (P4) on a region indicating bleeding. The target blood vessel region Ob {Ln} reaching the aorta 1 from the initial region (P3 or P4) is extracted from the volume data Vol {Ln} of the three-dimensional image generated in the image forming step (s1). Further, the target blood vessel region Ob {Ln} may be extracted from the main region Main {Ln}.

  The processing procedure is the same as in the first embodiment, and the history-added region expansion process is performed until a volume pixel included in the restored main region RMain {Ln} indicating only the aorta from the initial region (P3 or P4) is found. When the path P {PSk} is found, the restored main area RMain {Ln} and the affected area C are found until points that are not included in the main area Main {Ln} are found for all the volume pixels included in the path. The area expansion process is performed until {Ln} is reached.

  Further, the area expansion process may be performed a predetermined number of times. This is because the area expansion process does not select a volume pixel whose pixel value is smaller than VPth, and therefore does not become thicker than the target blood vessel area is imaged. The area expansion process with history is the same as the area expansion process with history described in the first embodiment.

  By such processing, the target blood vessel region Ob {Ln} is obtained. FIG. 16G shows the branch blood vessel 20 connecting the point (P3) on the blood vessel and the aorta 1 obtained as the target blood vessel region Ob {Ln}, and the point (P4) on the region showing bleeding and the aorta. 1 illustrates a branch blood vessel 30 connecting 1 to the other.

(Image display step (s11))
In this process, the data of the target blood vessel region Ob {Ln} extracted and generated in the target blood vessel extraction step (s21) is displayed on the image display unit 90 as a three-dimensional image. By such processing, the branch blood vessel 20 that connects the point (P3) on the blood vessel and the aorta 1 and the branch blood vessel 30 that connects the point (P4) on the region showing bleeding and the aorta 1 can be displayed.

  Heretofore, an example of the image processing apparatus, the image processing method, and the computer program according to the present embodiment has been described in detail. The image processing apparatus according to the present embodiment is not limited to the above example, and various modifications may be added. For example, when there are a plurality of bleeding regions in the three-dimensional image data Vol {Ln} generated from the tomographic image group Set {Ln}, as shown by the broken line in FIG. 15, the image display step (s11). Thereafter, it is possible to return to the start point setting step (s20) and specify another start point.

  Thereafter, as described above, the steps up to the target blood vessel extraction step (s21) or the steps up to the image display step (s11) are repeated a plurality of times as many as the number of the bleeding regions, whereby the target blood vessels connected to each bleeding region. Can also be extracted.

  Note that, as shown in the first embodiment, the method of obtaining the affected area C {Ln} by the user specifying the set point P2 and the radius r of the area to be expanded is the start point setting step (s20 in this embodiment). ). That is, when the start point (P3, P4) is obtained in the start point setting step (s20), the user inputs the range (radius r) of the affected area C {Ln}. Subsequent processing is the same as in the first embodiment. That is, the present invention shown in the first embodiment can be applied to a point on a blood vessel or a region showing bleeding shown in this embodiment instead of a tumor.

  According to the present invention, by processing the three-dimensional image data obtained from the tomography of the subject, it is possible to selectively display blood vessels from the aorta to the path to the tumor or bleeding site. According to the image obtained in this way, the position of the blood vessel leading to the path to the tumor, bleeding site, etc. can be identified using the aortic bifurcation point, bending point, or rib image as a landmark. Can be simplified. As a result, radiologists with special skills do not need to read the blood vessels that connect the aorta to the tumor or bleeding site over time, and the positions of these blood vessels can be clearly shown regardless of individual skills. it can.

  Therefore, by using the image processing apparatus and the image processing method according to the present invention, it is possible to find blood vessels up to the target tumor and deliver the anticancer agent only to the tumor without depending on individual skills. In addition, it is possible to specify a blood vessel between the bleeding portion and the artery and efficiently perform hemostasis treatment.

DESCRIPTION OF SYMBOLS 1 Aorta 2 Branch vessel 3 Tumor-free organ 4 Tumor-containing organ 5 Rib 6 Spine 8 Tumor 20, 30 Branch vessel 50 Calculation control unit 60 Main storage unit 70 Auxiliary storage unit 71 Image processing program 80 Input unit 90 Image display unit 100 , 200 Image processing apparatus 101 Image forming unit 102 Center extraction unit 103 Main region extraction unit 104 Contraction processing unit 105 Core unit extraction unit 106 Expansion processing unit 107 Tumor position setting unit 207 Start point setting unit 108 Tumor region extraction unit 109 Overlapping region detection unit DESCRIPTION OF SYMBOLS 110,210 Target blood vessel extraction part 150 Medical image storage apparatus 160 Medical image diagnostic apparatus 1000 Medical image diagnostic system

Claims (18)

An image forming unit that creates an entire area to be 3D image data from a plurality of tomographic images;
A main region extraction unit for extracting a main region connected to the aorta from the entire region; and an aorta region extraction unit for extracting an aorta region indicating only the aorta from the main region;
An affected area extraction unit for obtaining an affected area including the affected area from the entire area;
An image processing apparatus comprising: a target blood vessel extraction unit that obtains a target blood vessel region that connects the affected area to the aorta region. The main area extraction unit includes:
Binarizing one of the tomographic images,
A distance conversion process is performed to obtain a distance to a point where the nearest pixel value is zero for each pixel on the binarized tomographic image,
The image processing apparatus according to claim 1, wherein the main region connected to the aorta is extracted from the central pixel by a region expansion process using a volume pixel including the pixel having the largest distance as a central pixel. The aorta region extraction unit is
Shrinking the main region a predetermined number of times,
Perform a region expansion process on the contracted main region from the center pixel to obtain a core region,
The image processing apparatus according to claim 2, wherein the core region is expanded a predetermined number of times. The affected area extraction unit
The image processing apparatus according to any one of claims 1 to 3, wherein an affected area volume pixel included in the affected area is designated from the main area, and the affected area is designated as the affected area. The target blood vessel extraction unit
Perform area expansion processing while leaving a history of area expansion from the affected area to the aorta area,
5. The image processing apparatus according to claim 4, wherein a volume pixel along an expansion history from the affected area to the aorta area is subjected to area expansion processing to obtain the target blood vessel area. The affected area extraction unit
4. The affected part volume pixel included in the affected part is designated from the whole area, and the affected part area is obtained from the affected part volume pixel by a region extension process a predetermined number of times. The image processing apparatus described in 1. The affected area extraction unit
4. The affected area volume pixel included in the affected area is designated from the entire area, and the volume pixel included in a predetermined range from the affected area volume pixel is included in the affected area. 5. An image processing apparatus according to claim. The target blood vessel extraction unit
Find the overlapping area that is the common part of the affected area and the main area,
Perform region expansion processing while leaving a history of region expansion from the overlapping region to the aorta region,
Volume expansion process along the expansion history from the affected area to the aorta area, area expansion processing,
The image processing apparatus according to claim 6, wherein a target blood vessel region is obtained. An image forming process for creating an entire area to be three-dimensional image data from a plurality of tomographic images;
A main region extraction step for extracting a main region connected to the aorta from the entire region; and an aorta region extraction step for extracting an aorta region indicating only the aorta from the main region;
An affected area extracting step for obtaining an affected area including the affected area from the entire area;
And a target blood vessel extraction step for obtaining a target blood vessel region connecting the affected area to the aorta region. The main area extraction step includes:
Binarizing one of the tomographic images,
A distance conversion process is performed to obtain a distance to a point where the nearest pixel value is zero for each pixel on the binarized tomographic image,
10. The image processing method according to claim 9, wherein the main region connected to the aorta is extracted from the center pixel by region expansion processing using a volume pixel including the pixel having the largest distance as a center pixel. The aorta region extraction step includes
Shrinking the main region a predetermined number of times,
Perform a region expansion process on the contracted main region from the center pixel to obtain a core region,
The image processing method according to claim 10, wherein the core region is expanded a predetermined number of times. The affected area extraction step includes
12. The image processing method according to claim 9, wherein an affected area volume pixel included in the affected area is designated from the main area, and the affected area is designated as the affected area. The target blood vessel extraction step includes:
Perform area expansion processing while leaving a history of area expansion from the affected area to the aorta area,
13. The image processing method according to claim 12, wherein a volume pixel along an expansion history from the affected area to the aorta area is subjected to area expansion processing to obtain the target blood vessel area. The affected area extraction step includes
12. The affected part volume pixel included in the affected part is designated from the entire area, and the affected part area is obtained from the affected part volume pixel by a region expansion process a predetermined number of times. The image processing method described in 1. The affected area extraction step includes
12. The affected area volume pixel included in the affected area is designated from the entire area, and a volume pixel included in a predetermined range from the affected area volume pixel is included in the affected area. 12. An image processing apparatus according to claim. The target blood vessel extraction step includes:
Find the overlapping area that is the common part of the affected area and the main area,
Perform region expansion processing while leaving a history of region expansion from the overlapping region to the aorta region,
Volume expansion process along the expansion history from the affected area to the aorta area, area expansion processing,
The image processing method according to claim 14, wherein a target blood vessel region is obtained.   A program for causing a computer to execute the image processing method according to claim 9.   A recording medium on which the program according to claim 17 is recorded.

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