JP6021340B2 - Medical image processing apparatus and control program - Google Patents

Description

  Embodiments described herein relate generally to a medical image processing apparatus.

Currently, X-ray CT (Computed Tomography)
Image diagnosis is often used in which a subject is imaged using a modality such as an apparatus or MRI (Magnetic Resonance Imaging) apparatus, and a disease or the like is diagnosed using a three-dimensional image. When an image is displayed on the two-dimensional screen from the three-dimensional image data, the multi-section reconstruction (MPR: Multi Planer Reconstruct)
ion) method, addition averaging method, maximum value projection (MIP: Maximum Intensity)
Projection method, minimum projection (MinIP: Minimum Intensi)
ty Projection) method and the like are used. These techniques are diverse and need to be used according to the application.

Here, rendering is a method of creating a realistic image on a two-dimensional screen from three-dimensional image data created by three-dimensional data construction.

The maximum value projection method is a technique often used when imaging blood vessels after the injection of contrast medium.
The pixel value of a blood vessel having a large linear absorption value becomes high and comes out, and it becomes difficult to see an organ around the blood vessel. On the other hand, when imaging is performed by the averaging method, the average value of the blood vessel and organ pixels is obtained, and the thin blood vessels are not so noticeable. It is also possible to check the blood vessels and organs on the same screen by combining the maximum projection image and the addition average image that can confirm each blood vessel and organ firmly and changing their composition rate as necessary. ,
This takes time and effort during diagnosis.

In addition, blood vessels meander in three dimensions, and the imaging conditions may be changed even after an image has been generated once in order to check its running condition. At this time, if the blood vessel is to be observed under a desired condition, the condition of the organ region also changes at the same time.

Therefore, there is a current need for a technique for acquiring images obtained by satisfactory imaging of both blood vessels and organs and setting each condition individually.

JP 2007-14474 A

The problem to be solved by the present invention is to provide a method for obtaining a desired image capable of satisfactorily confirming both a thin blood vessel and an organ around the blood vessel.

In order to solve the above-described problem, a medical image processing apparatus according to an embodiment uses any one of at least two rendering methods for converting three-dimensional image data into two-dimensional image data. Or two-dimensional image data obtained by converting data at a plurality of positions in the three-dimensional image data based on the determination unit for determining each unit region in the two-dimensional region and the rendering method determined for each unit region. An image generation unit that generates the data, and the determination unit is data at a point of interest set in a maximum value projection image generated based on an arbitrary tomographic image obtained from the three-dimensional image data; The rendering method is determined based on data at points in the vicinity of the point of interest.

It is a block diagram which shows the whole structure of the diagnostic imaging apparatus in 1st Embodiment and 2nd Embodiment. It is a block diagram showing the whole structure about image processing in a 1st embodiment. It is the schematic which showed a mode that a predetermined threshold value and the maximum pixel value in 1st Embodiment were compared, and a pixel value was determined with a different rendering method based on the result. It is a figure which shows the tomographic image in each rendering method and the image which has those characteristics in 1st Embodiment. It is a flowchart in the case where a pixel value is determined for each unit region and an image is generated in the first embodiment. It is the schematic which showed the change of the blood-vessel part contained in three-dimensional image data when changing thickness in 1st Embodiment. It is the block diagram which showed the whole structure regarding image processing in 2nd Embodiment. It is a flowchart in 2nd Embodiment. It is the schematic which showed the mode of the connection area | region extraction in 2nd Embodiment.

  Hereinafter, embodiments for carrying out the invention will be described.

(First embodiment)
In describing the medical image processing apparatus of the first embodiment, an outline of a mechanism for obtaining a medical image will be described with reference to FIG. 1, taking an X-ray CT apparatus as an example.

FIG. 1 is a configuration diagram showing the overall configuration of an X-ray CT apparatus for obtaining an image for performing synthesis processing in the present embodiment.

In FIG. 1, the diagnostic imaging apparatus 10 is, for example, an X-ray CT apparatus or an MRI apparatus. Here, an X-ray CT apparatus is taken as an example. The X-ray CT apparatus includes a gantry 11, and a rotating ring 12 is provided in the gantry 11 and is rotated by a rotating mechanism (not shown). Rotating ring 1
2, an X-ray tube 13 and an X-ray detector 14 are arranged to face each other, the central portion of the rotating ring 12 is opened, and a subject P placed on a top plate 15 provided in a bed is provided there. Inserted.

The X-rays exposed from the X-ray tube 13 pass through the subject P, attenuate and are detected by the X-ray detector 14, and then converted into an electrical signal, amplified by the data collection unit 16, and converted into digital data Then, the projection data is transmitted via the data transmission device 17.

In the computer system 20, first, data from the data transmission device 17 is sent to the preprocessing unit 21. The preprocessing unit 21 performs processing such as signal intensity correction and signal loss correction, and outputs projection data onto the bus line 300. On the bus line 300, the system control unit 22
An operation unit 23 such as a keyboard, a data processing unit 24, a data storage unit 25 for storing data, an image processing unit 31 having a reconstruction processing unit 26, an image display unit 27, and the like are connected.

The system control unit 22 controls the operation of each unit of the computer system 20 and controls the gantry driving unit (not shown) and the X-ray control unit (not shown). The data storage unit 25 stores data such as tomographic images. The reconstruction processing unit 26 reconstructs tomographic image data based on the projection data. The data processing unit 24 performs various data processing using the data stored in the data storage unit 25. The image display unit 27 displays medical images and the like, and the operation unit 23 is operated by an operator such as a doctor to input various instructions for image processing, and information such as the state of the subject P and the examination method. input.

Projection data captured by the diagnostic imaging apparatus 10 and transmitted via the data transmission apparatus 17 is:
The tomographic image data is corrected by the preprocessing unit 21, the tomographic image data is reconstructed by the reconstruction processing unit 26, and the three-dimensional image data based on the reconstructed image is stored in the data storage unit 25. Various image data are generated using the three-dimensional image data.

FIG. 2 is a block diagram showing an overall configuration related to image processing in the present embodiment. Each element corresponds to that in FIG. 1, and the image synthesis process is performed by the data processing unit 24. The data processing unit 24 includes a first condition setting unit 30, a comparison unit 32, a first image generation unit 36, a second image generation unit 37, a third image generation unit 38, a third image storage unit 39, and a second condition setting unit 40. The third condition setting unit 41 is provided.

The first condition setting unit 30 sets a condition for thickness in a tomographic image having a tomographic thickness (hereinafter referred to as thickness), which is an arbitrary three-dimensional region, from the three-dimensional image data, when operated by an operator.
Here, the angle of the tomographic image set in the first condition setting unit 30 is the same regardless of the rendering method for generating the image, but different thicknesses may be set. Arbitrary angles and cross sections are set by, for example, reconstructing slice images of different cross sections such as sagittal images by performing interpolation processing from a plurality of slice images (axial images) taken in the direction of the body axis of the subject P. say.

The first image generation unit 36 generates a maximum value projection image based on an arbitrary tomographic image obtained from the three-dimensional image data by the first condition setting unit 30. Here, the maximum value projection image displays the maximum pixel value (CT value for CT and signal intensity for MRI) in each linear voxel as viewed from the direction perpendicular to the cross section of the tomogram. is there. That is, when the thickness changes, the number of linear pixels also changes, so that the maximum pixel value can change.

The second image generation unit 37 generates an addition average image based on an arbitrary tomographic image obtained from the three-dimensional image data by the first condition setting unit 30. Here, an addition average image is obtained by projecting and displaying the addition average value of all the pixel values in each linear voxel as viewed from the direction perpendicular to the cross section of the tomogram. Also in this case, as described above, when the thickness changes, the number of pixels on the straight line also changes, so that the addition average value can change.

The comparison unit 32 sets a threshold value as a comparison reference of pixel values in advance, compares the threshold value with the pixel value of the maximum value projection image obtained by the first image generation unit 36 for each unit region, and the result Based on
The blood vessel region is selected by the maximum value projection method, and the pixel value of the organ region is selected by the addition average method. Here, in this embodiment, it is assumed that a contrast medium is injected to image a blood vessel. That is, the blood vessel into which the contrast medium has been injected does not transmit much X-rays and has a high pixel value. Therefore, the blood vessel pixel value is likely to be the maximum pixel value among all the voxels on a certain straight line in the three-dimensional image data, so that the blood vessel region is easily extracted from the maximum value projection image. The pixel value for each unit area is compared with the threshold value, and the pixel value is determined for each unit area by the maximum value projection method or the addition average method, and the image generation is described. Details will be described later. One pixel may be used as a unit region, or a cluster formed by a plurality of pixels may be used as a unit region.

The third image generation unit 38 generates image data composed of pixel values selected for each unit region by a plurality of different methods such as a maximum value projection method and an addition average method.

The third image storage unit 39 stores the image data generated by the third image generation unit 38. If the condition is changed by the second condition setting unit 40 or the third condition setting unit 41, the change is saved.

The second condition setting unit 40 includes an area portion of the pixel value selected by the maximum value projection method, and a third
The condition setting unit 41 changes the condition of the area portion of the pixel value selected by the averaging method. The conditions here are window conditions (WW / WL: window width / window level) and thicknesses used when generating a maximum value projection image and an addition average image from three-dimensional data, respectively.

The WW / WL value is a parameter for adjusting display brightness and contrast in an image expressed in shades such as a maximum value projection image and an addition average image. For example, when a CT value is given in 16 bits (65536 gradations), cutting out a range particularly necessary for diagnosis and converting it to 8 bits (256 gradations) is called WW / WL conversion. The width of the cutout range at that time is called WW, and the center value of the cutout range is called WL.

The thickness refers to the thickness in the tomographic image cut out from the three-dimensional image data, and information on pixel values in the thickness range is referred to as thickness information. When observing a meandering blood vessel, information on the thickness is projected by giving the thickness, and the running state of the blood vessel is confirmed. Incidentally, since there is naturally only one pixel value when the thickness is one pixel, the maximum value projection image and the addition average image generated therefrom are the same image.

Here, the details of determining the pixel value by the maximum value projection method or the addition average method for each unit region will be described.

In the first condition setting unit 30, a tomographic image having an arbitrary thickness is obtained from the three-dimensional image data.
Then, the comparison unit 32 uses a predetermined arbitrary threshold value, and compares the maximum pixel value in the thickness for the maximum value projection image of each unit region with the threshold value. Thereby, the comparison unit determines whether to obtain the pixel value by the maximum value projection method or the pixel value by the addition averaging method for each unit region. Here, when the maximum pixel value in the thickness for the maximum value projection image is higher than the threshold value, the comparison unit 32 determines that the maximum pixel value in the unit region can correspond to the pixel value of the blood vessel. On the other hand, when the maximum pixel value is lower than the threshold, the comparison unit 32 determines that the addition average value obtained from the thickness for the addition average image in the unit region can correspond to the pixel value of the organ. The pixel values obtained here are regions other than blood vessels, such as organ regions.

Thereafter, based on the determination result of the comparison unit 32, the first image generation unit determines the pixel value in the unit region when the maximum pixel value in the maximum value projection image thickness is equal to or greater than the threshold value. Then, when the maximum pixel value in the maximum value projection image thickness is less than the threshold, the second image generation unit determines the addition average value in the addition average image thickness as the pixel value in the unit region.

Then, after the comparison unit 32 performs the above processing on all unit regions and the pixel values of the unit regions are determined by the first image generation unit and the second image generation unit, the third image generation unit obtains from different rendering methods. One image in which the region of the specified pixel value exists is generated. Hereinafter, the threshold value and the pixel value are compared for each unit region with reference to FIG.

FIG. 3 is a schematic diagram showing a state in which pixel values are compared with threshold values for each unit region. The unit area here is 1 pixel. As indicated by X1 to X4 and Y1 to Y4, each unit region is represented by (X,
The position is grasped by the information of Y), and the pixel value is shown in each place. For example, (X2, Y1) = 50. Here, for example, when determining the blood vessel region with the threshold value set to 50, when the pixel value of the unit region is 50 or more, the maximum pixel value is determined by the maximum value projection method,
If it is less than 50, the pixel value is determined by the addition averaging method. As a result, a single image in which the pixel value region of the blood vessel 100 is formed by the maximum value projection method and the pixel value region of the organ 200 is formed by the addition averaging method is obtained. That is, here, as shown in FIG. 3, (X2, Y1), (X2, Y2), (X
2, Y3), (X2, Y4), and (X3, Y4) determine the pixel value by the maximum value projection method, and the pixel values of the other unit areas by the averaging method.

FIG. 4 is a schematic diagram of an image generated by changing the method of determining pixel values for each unit area by the addition average method and the maximum value projection image, and the addition average method and the maximum value projection method used when obtaining these two images. FIG. FIG. 4A shows an addition average image, which makes the blood vessel 100 thin and difficult to see. On the other hand, FIG. 4B is a maximum-value projection image, and although the blood vessel 100 is visible, the features of the surrounding organ 200 are lost or difficult to see. FIG. 4C shows the result of processing based on these images by the above-described method using the threshold value. The defects in the images in FIGS. 4A and 4B are compensated, and both the blood vessel 100 and the organ 200 are easily observed.

  Next, the operation of the medical image processing apparatus according to this embodiment will be described.

  FIG. 5 is a flowchart of this embodiment.

In step S <b> 1, the first condition setting unit 30 sets a tomography and thickness to be cut out in the three-dimensional image data including a plurality of voxels obtained by the X-ray CT apparatus and stored in the data storage unit 25. Here, the angle of the slice to be cut out is the same for the maximum value projection image and the addition average image, but different thicknesses are set.

In step S2, the comparison unit 32 compares the maximum pixel value for each unit region with a preset threshold value in the cross section of the tomography set in step S1.

Here, the cross section of the fault means a plane perpendicular to the thickness direction of the fault, and it is assumed that thickness information is contained in a unit area on the plane.

Then, the pixel value region (Y) exceeding the threshold value goes to step S3A. For the pixel value region (N) whose maximum pixel value is less than the threshold value, go to Step S3B. At this time, the maximum pixel value to be compared with the threshold value is taken from the thickness range for the maximum value projection image.

In step S3A, the first image generation unit 36 determines the maximum pixel value as the pixel value in the unit region for the unit region in which the maximum pixel value has exceeded the threshold value in step S2. Since the maximum pixel value is determined from the thickness information in the three-dimensional image data, a blood vessel region having a high pixel value is clearly displayed.

In step S <b> 3 </ b> B, the second image generation unit 37 determines the pixel value by the averaging method for the unit area whose maximum pixel value is less than the threshold value in step S <b> 2. Since the pixel value is obtained by the averaging method using a thickness different from that used in step S3A, the organ region is clearly displayed.

In step S4, the comparison unit 32 determines whether step S2 has been performed on all unit regions of the cross section obtained in step S1. If there is a unit area for which step 2 has not been performed yet, (X, Y) is updated to (X + 1, Y) or (X, Y + 1) and the process returns to step S2. If step S2 is already applied to all unit areas, step S5 is performed.
What.

In step S5, the third image generation unit 38 uses a different method (maximum value projection method or addition averaging method) for each unit region based on the pixel values determined for each unit region in steps S3A and S3B. An image composed of the pixel values obtained by the above is generated, and the image is sent to the third image storage unit 39. Here, even if there is a change in WW / WL after image generation, the third image generation unit 38 generates an image again, and the image is sent to the third image storage unit 39.

In step S6, the third image storage unit 39 stores the image generated in step S5, and proceeds to step S7.

In step S7, the image display unit 27 displays the image stored in the third image storage unit 39 in step S5. Then, go to step S8.

In step S8, the second condition setting unit 40 and the third condition setting unit 41 change at least one WW / WL in the maximum value projection image or the addition average image when an instruction is issued from the instruction unit 23. To do. When changing WW / WL (Y), go to step S5. If not changed (N), go to step S9.

In step S <b> 9, the second condition setting unit 40 and the third condition setting unit 41 determine the pixel value region determined by the maximum value projection method or the addition averaging method when there is an instruction from the instruction unit 23. The thickness of at least one of the pixel value regions is changed. If there is a change in either thickness (Y), go to step S1. If there is no change in the thickness in either area, the process ends (N).

Here, since the range of pixel values for comparison with the threshold value changes when the thickness is changed, it is necessary to return to step S1 again. In step S1, based on the thickness condition set in step S9, the first condition setting unit 30 changes the thicknesses for the maximum value projection image and the addition average image and reflects them in the tomographic image. Step S2 and subsequent steps are the same as described above.

Incidentally, when only the thickness of the addition average image is changed, the result of the comparison between the maximum pixel value and the threshold value in step S2 is not different from the previous result, so step S2 is skipped and step S2 is skipped.
You may proceed to B. (Dotted line part)
A schematic diagram regarding the thickness change is shown in FIG.

In FIG. 6, the blood vessel 100 travels up and down in the three-dimensional image data. In FIG. 6A, a thickness including only a part of the upper portion of the blood vessel 100 is set. When the thickness is changed here, the curved portion of the blood vessel 100 becomes the observation range as shown in FIG. 6B, and as a result, the maximum pixel value and the addition average value in each unit region are also changed. This is why it is necessary to compare the threshold value with the pixel value in each unit area again.

Here, in step S3A and step S3B, an example is shown in which comparison is made for each unit region, and each time the pixel value is determined in the unit region by the maximum value projection method or the addition average method. And the first image generation unit 36 and the second image generation unit 3
In step 7, a maximum value projection image and an addition average image may be generated. That is, two images having a relationship of interpolating each other in a region where no pixel exists may be generated, and then the third image generation unit 38 may combine the two images.

Here, the threshold value provided for comparison with the maximum pixel value may be set as the threshold value range by setting an upper limit. This is to prevent imaging of bones or the like having a higher pixel value than the blood vessel 100 existing in the linear voxel as viewed from the viewing direction in the thick three-dimensional image data. In other words, a range that is greater than or equal to the expected blood vessel pixel value and less than the pixel value such as bone is set as the threshold range.

In addition, the threshold value and the maximum pixel value are first compared from the three-dimensional image data, and the method of generating the maximum value projection image and the addition average image for each unit region from the result has been described. However, the present embodiment is not limited to this. There is nothing. For example, first, a maximum value projection image and an addition average image may be generated from three-dimensional image data, a blood vessel region and an organ region may be extracted from these images, and then a synthesis process may be performed. Specifically, two types of threshold values are prepared for the maximum value projection image and the addition average image, and different values are set for the threshold values. That is, by setting different values, a blood vessel region having a high pixel value is extracted from the maximum value projection image, while an organ region having a low pixel value is extracted from the addition average image.

Further, it is not always necessary to extract both the blood vessel 100 and the organ 200 by comparing them with the threshold values. For example, if a blood vessel region having a pixel value higher than the threshold value is extracted from the maximum value projection image, the region may be deleted from one of the addition average images using the region information. Thereby, it is possible to obtain an addition average image that leaves only the organ region.

By combining the above-described blood vessel region image extracted from the maximum value projection image and the organ region image extracted from the addition average image, it is possible to generate an image in which blood vessels and organs are clearly shown.

In the present embodiment, the maximum value projected image and the addition average image are used as examples. However, the present embodiment is not limited to this, and the present embodiment can be applied to an image using another rendering method. For example, as shown in the present embodiment, when imaging a blood vessel by injecting a contrast medium, the maximum value projection method is suitable because of a high pixel value, but the minimum value projection method may be used depending on other imaging regions. It may be suitable to use. For example, when imaging the pancreatic duct, the minimum value projection technique is effective.

  Next, effects of the medical image processing apparatus according to the present embodiment will be described.

As described above, the blood vessel 1 can be obtained by using the medical image processing apparatus according to the present embodiment.
A diagnostic image capable of satisfactorily observing both 00 and the organ 200 can be obtained. They are realized by combining multiple images with different rendering methods. In addition, since the synthesized image can be adjusted in thickness and WW / WL for each of the maximum-value projection image or the addition average image, the diagnostician recognizes both the blood vessel region and the organ region with good contrast according to the diagnosis. Desired observations can be made. For example, by changing the thickness of only the image of the blood vessel region, that is, the maximum value projection image, it is possible to confirm the travel of the blood vessel as shown in FIGS. 6 (a) and 6 (b). The blood vessel region after the thickness change can be displayed. Thereby, the diagnostician can observe a desired part with a desired image quality on one image.

Here, when observing an organ, it is better for observation to make the thickness not too thick,
Reduce the thickness. On the other hand, in order to see the running of the blood vessel using the maximum value projection image, it is preferable that the thickness is at least larger than the thickness of the addition average image for organ observation. Therefore, this embodiment which can change conditions for every image in each method is very useful.

  Finally, the conventional problem and the effect of the present embodiment are summarized.

In imaging the blood vessel 100 with the X-ray CT apparatus, the contrast agent is injected into the blood vessel 100 and then X
The line is exposed to the subject and imaged. As a result, the portion through which the contrast agent has permeated has the effect of suppressing the transmission of X-rays and enhancing the contrast in the image. However, in the case of an image obtained only by the conventional averaging method, it is attracted to other pixel values of the unit region having a thickness, and the blood vessel 10
0 is not very noticeable. On the other hand, when an image is generated by the maximum value projection method, the organ 200 portion does not become a desired image.

Therefore, the medical image obtained by the present embodiment in which the blood vessel 100 part and the organ 200 part are displayed as appropriate pixel values in different images by different methods is very useful at the time of diagnosis, and the doctor can appropriately use the medical image based on the medical image. It is possible to perform various medical practices.

(Second Embodiment)
Next, a second embodiment will be described with reference to FIG.

FIG. 7 is a block diagram showing an overall configuration related to image processing in the medical image processing apparatus of the second embodiment. Compared with the first embodiment, the method of extracting a specific area is different, but after that, an image determined by the maximum value projection method or the addition average method is generated for each area, and the window condition and thickness are determined for each area. The point of changing is the same.

The medical image processing apparatus according to the second embodiment includes a part designating unit 35 for extracting a region.

The part designating unit 35 designates a unit area in the image. This may be performed by the operator using a pointing device such as a mouse.

Compared to the first embodiment, the method for extracting a specific region from the maximum value projection image is different. Rather than extracting a region by comparing with pixel values by providing a threshold value, the region designating unit 35 designates a certain point as a point of interest and extracts a neighboring pixel value at the point of interest by a method such as connected region extraction. Extract regions. Here, the neighborhood pixel value means a pixel value in a surrounding unit area when a certain unit area is designated as a point of interest.

  FIG. 8 is a flowchart in this embodiment.

In step S10, the first condition setting unit 3 is similar to step S1 of the first embodiment.
0 sets a thick tomographic image from the three-dimensional image data. The difference from the first embodiment is that the first image generation unit 36 generates a maximum-value projection image in the next step S11.

In step S11, the first image generation unit 36 generates a maximum value projection image from the tomographic image obtained in step S10.

In step S12, the part designating unit 35 designates an arbitrary area in the maximum value projection image. In the present embodiment, a region having a high pixel value in the maximum-value projection image, that is, a blood vessel region is extracted by a technique such as connected region extraction. The connected region extraction is a method in which when at least one point is designated as a point of interest in a certain region, connected pixels are extracted from the point of interest. Then, a set of pixels in which pixel values in an arbitrary range close to the pixel value of the designated attention point are selected from the pixels near the designated attention point is extracted as a connected region.

FIG. 9 is a schematic diagram showing how a specific area is extracted by a connected area extraction method. When a pixel value at a certain point is designated by the part designating unit 35, an area connected to the pixel value can be extracted. As shown in FIG. 9, the area may be specified by the operator specifying the trigger point on the image display unit 27 using the cursor 42, or any pixel close to the pixel value of the area to be extracted. A value may be registered. Here, a pixel value close to the blood vessel 100 is registered in order to extract the region of the blood vessel 100 from the maximum value projection image.

In step S13, the comparison unit 32 determines for each pixel value whether the region is designated in step S12 or not. Here, the steps are divided into two steps for the designated area and the other areas. Region with high pixel value (blood vessel region) by connected region extraction method
Is designated (Y), step S1 is performed for the maximum value projection image of the designated area.
Go to 4A. And about the area | region (organ area | region) other than the designated area | region, it progresses to step S14B.

In step S14A, the first image generation unit 36 is determined in step S13.
The specified region is extracted to generate a maximum value projection image.

In step S14B, based on the information determined in step S13, step S14
For the regions other than the region extracted in step 15, the addition average image is generated from the thickness information for the addition average image. Specifically, for the region other than the blood vessel region extracted by the connected region extraction method in step S13, an addition average image is generated from the three-dimensional image data considering the thickness for the addition average image.

In step S15, the third image generation unit 38 performs a combining process on the maximum value projection image obtained in steps S14A and the addition average image obtained in step S14B, and generates a combined image. Subsequent window condition changes and thickness changes are the same as in the first embodiment.

In step S16, the third image storage unit 39 stores the composite image generated in step S15, and proceeds to step S17.

In step S17, the image display unit 27 displays the composite image stored in the third image storage unit 39 in step S16. Then, go to step S18.

In step S18, the second condition setting unit 40 and the third condition setting unit 41 are instructed by the instruction unit 23.
When there is an instruction from, in the maximum value projection image or the addition average image, at least one of the thicknesses of the three-dimensional image data from which those images are generated is WW /
Change WL. When changing WW / WL (Y), go to step S15. If not changed (N), go to step S19.

In step S <b> 19, the second condition changing unit 40 and the third condition changing unit 41 include the instruction unit 23.
When there is an instruction from, the thickness of at least one of the maximum value projected image or the addition average image is changed. If there is a change in thickness (Y), go to step S10. If there is no change (N), the process ends.

Here, an example in which a blood vessel region having a high pixel value is extracted in the connected region extraction has been described, but conversely, a region having a low pixel value may be similarly connected and extracted. In that case, an addition average image is first generated from the three-dimensional image data, and a connected region is extracted therefrom. Then, a region having a low pixel value other than the blood vessel is extracted, and a maximum value projection image is generated from the remaining region. By doing so, the blood vessel region can be extracted by the maximum value projection image and the organ regions other than the blood vessels can be extracted by the addition average image, as in the case of extracting the blood vessel region from the maximum value projection image by the connected region extraction. Also, by synthesizing them, it is possible to obtain a desired image that can be easily observed in both the blood vessel region and the organ region.

  Next, effects of the medical image processing apparatus according to the present embodiment will be described.

This embodiment is different in area extraction method from the first embodiment, and it is not necessary to perform comparison with a predetermined threshold value. Area extraction and synthesis can be performed by an operator's own operation. it can. Thereby, for example, when the extraction / combination is performed once and the operator feels that the extracted connected areas are insufficient, an area to be extracted can be set by designating an arbitrary area again. As a result, the diagnostician can obtain a desired image.

Although several embodiments have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

P Subject 10 Diagnostic imaging device (X-ray CT device)
11 Gantry 12 Rotating ring 13 X-ray tube 14 X-ray detector 15 Top plate 16 Data collection unit 17 Data transmission device 20 Computer system 21 Preprocessing unit 22 System control unit 23 Operation unit 24 Data processing unit 25 Data storage unit 26 Reconfiguration Processing unit 27 Image display unit 30 First condition setting unit 31 Image processing unit 32 Comparison unit 35 Site designation unit 36 First image generation unit 37 Second image generation unit 38 Third image generation unit 39 Third image storage unit 40 Second Condition setting unit 41 Third condition setting unit 42 Cursor 100 Blood vessel 200 Organ

Claims (9)

A discriminating unit that discriminates for each unit region in the two-dimensional region, which rendering method is used for conversion among the at least two types of rendering methods for converting the three-dimensional image data into the two-dimensional image data,
An image generation unit that generates the two-dimensional image data by converting data at a plurality of positions in the three-dimensional image data based on the rendering method determined for each unit region;
The determination unit is based on data at a point of interest set in a maximum value projection image generated based on an arbitrary tomographic image obtained from the three-dimensional image data, and data at a point in the vicinity of the point of interest. A medical image processing apparatus for determining a rendering method. The determination unit includes: a pixel value at a point near the pixel values and the point of interest at the point of interest, based on a comparison of the preset pixel value range, the medical image processing according to claim 1, wherein performing the determination apparatus. A discriminating unit that discriminates for each unit region in the two-dimensional region, which rendering method is used for conversion among the at least two types of rendering methods for converting the three-dimensional image data into the two-dimensional image data,
An image generation unit that generates the two-dimensional image data by converting data at a plurality of positions in the three-dimensional image data based on the rendering method determined for each unit region;
The image generation unit sets a predetermined thickness region corresponding to each of the determined rendering methods for the three-dimensional image data, and based on the three-dimensional image data in the thickness region, the two-dimensional A medical image processing apparatus for generating image data. The medical image processing apparatus according to claim 3, wherein the determination unit performs determination to determine a pixel value based on a maximum pixel value in all voxels on a certain straight line in the three-dimensional image data. The medical image processing apparatus according to claim 1, wherein the rendering method includes at least a maximum value projection method and an addition average method. 3. The apparatus according to claim 1, further comprising: a condition changing unit that changes a window condition or a thickness condition for each of the unit regions whose pixel values are determined by any one of the at least two rendering methods. The medical image processing apparatus described. 5. The medical image according to claim 4, wherein the determination unit determines a type of the rendering method for determining the pixel value by setting an arbitrary threshold and comparing the threshold with the maximum pixel value. Processing equipment. In medical image processing device,
A discriminating means for discriminating, for each unit region in the two-dimensional region, which one of the rendering methods for converting the three-dimensional image data into the two-dimensional image data is used for the conversion.
Based on the rendering method determined for each unit region, the image generating means for converting the data of a plurality of positions in the three-dimensional image data to generate the two-dimensional image data is operated,
The determination means is based on data at a point of interest set in a maximum value projection image generated based on an arbitrary tomographic image obtained from the three-dimensional image data, and data at a point in the vicinity of the point of interest. Control program to determine the rendering method. In medical image processing device,
A discriminating means for discriminating, for each unit region in the two-dimensional region, which rendering method is used among at least two types of rendering methods for converting the three-dimensional image data into the two-dimensional image data,
Based on the rendering method determined for each unit region, the image generating means for converting the data of a plurality of positions in the three-dimensional image data to generate the two-dimensional image data is operated,
The image generation means sets a predetermined thickness region corresponding to each of the determined rendering methods for the three-dimensional image data, and based on the three-dimensional image data in the thickness region, the two-dimensional A control program that generates image data.

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