JPH08336503A - Medical image diagnostic system - Google Patents

Abstract

PURPOSE: To accurately estimate the contour of a region of interest and to sample the contour without accurately designating a point by providing a sampling window designating means which lets an operator to designate a sampling window and a contour sampling means which samples the contour of the region of interest. CONSTITUTION: An image processing device 2 is equipped with a control part 2a, a sampling window selection part 2b, a sampling window adjusting part 2c, a threshold value range decision part 2d, a binarization processing part 2e and a contour decision part 2f. The control part 2a receives an image to be diagnosed from an image pickup device 1, and stores it in a memory device 3. The sampling window selection part 2b lets the operator to select one of plural kinds of sampling windows prepared in advance. The threshold value range decision part 2d decides a threshold value from the value of a picture element in the sampling window, and delivers it to the binarization processing part 2e. The binarization processing part 2e makes the sampling window into a binary image by using the threshold value, and delivers it to the contour decision part 2f. The contour decision part 2f samples the contour from the binary image.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a medical image diagnostic apparatus, and more particularly to a medical image diagnostic apparatus capable of accurately and quickly extracting the contour of a region of interest in a diagnostic image.

[0002]

2. Description of the Related Art In order to diagnose a heart disease, MRI (Ma
gnetic Resonance Imaging) and CT (Computed
The movement of the myocardial wall is observed using a diagnostic image acquired for the chest with a Tomography device or the like. At that time, the contour extraction processing is performed to extract the contour of the myocardial wall on the diagnostic image.

FIG. 11 is a flowchart of an example of conventional contour extraction processing. At Step J1, N diagnostic images to be subjected to the contour extraction processing are read in accordance with a command from the operator. At step J2, as shown in FIG.
The diagnostic image G1 is displayed. K1 in the figure is a region of interest, such as the left ventricle of the heart. At step J3, the operator, as shown in FIG. 13, receives the first diagnostic image G1.
Three input points p1 to p3 are designated at positions estimated to be contours of the region of interest K1. At step J4, as shown in FIG. 14, a center point C of the input points p1 to p3 is calculated, and a plurality of radial scanning lines L, L, ...
Determine. At step J5, the number counter i is set to "1".
Initialize to. In step J6, as shown in FIG. 15, contour points m, m, ... On each scanning line L, L, ... Of the i-th diagnostic image are obtained. In step J7, as shown in FIG. 16, the input points p1 to p3 on the first diagnostic image G1 and the contour points m, m, ... Are connected to determine the first contour R1. In steps J8 and J9, the above steps J6 and J7 are repeated for the second diagnostic image and thereafter, and the second contour to the Nth contour are determined. The above-mentioned conventional technique is described in “Automatic Extraction of Left Ventricular Contour from MRI Image, Shinichi Utsunomiya, Review of Shimadzu, Vol.
・ 2 (1994.9) ”.

[0004]

In the above-described conventional contour extraction processing, the operator estimates the contour of the region of interest while looking at the diagnostic image and designates three points on the contour. However, it is not easy to accurately estimate the contour of the unclear region of interest while looking at the diagnostic image, and there is a problem that the estimation result varies. Further, it is not easy to accurately specify a point on the contour of the unclear region of interest, and there is a problem that the specification result varies. Further, there is a problem that the extraction result also varies due to the variation. Therefore, an object of the present invention is to accurately estimate the contour of a region of interest,
An object of the present invention is to provide a medical image diagnostic apparatus capable of extracting a contour without accurately specifying a point.

[0005]

SUMMARY OF THE INVENTION In a first aspect, the present invention is a medical image diagnostic apparatus having a contour extracting function for extracting the contour of a region of interest in a diagnostic image, which displays the diagnostic image and displays the region of interest. An extraction window designating means for allowing an operator to designate an extraction window including the above; and a contour extracting means for extracting a contour of the region of interest based on a pixel value of a pixel included in the extraction window. An image diagnostic apparatus is provided.

According to a second aspect of the present invention, in the medical image diagnostic apparatus having the above structure, the extraction window designating means allows the operator to select one of a plurality of types of extraction windows. And an extraction window adjusting means for allowing the operator to adjust the size and position of the selected extraction window.

According to a third aspect of the present invention, in the medical image diagnostic apparatus having the above structure, the contour extracting means determines a threshold range based on pixel values of pixels included in the extraction window. And binarization processing means for obtaining a binary image within the extraction window based on the threshold range, and contour determining means for determining the contour of the region of interest by the binary image. A medical image diagnostic apparatus is provided.

According to a fourth aspect of the present invention, in the medical image diagnostic apparatus having the above-mentioned structure, the extraction window designating means is provided with a plurality of diagnostic images of the same target taken at different times or at different sites close to each other. Displaying a first diagnostic image out of a plurality of captured diagnostic images and prompting an operator to specify an extraction window including a region of interest;
First, for the first diagnostic image, the contour of the region of interest is extracted based on the pixel values of the pixels included in the extraction window, and for the second and subsequent diagnostic images, the contour of the region of interest is extracted based on the previously extracted contour. There is provided a medical image diagnostic apparatus characterized by extracting each of

[0009]

In the medical image diagnostic apparatus according to the first aspect,
The diagnostic image is displayed and the operator is made to specify the extraction window including the region of interest. Further, when the operator specifies the extraction window, the contour of the region of interest is extracted based on the pixel values of the pixels included in the extraction window. Therefore, the operator may roughly estimate the contour of the region of interest while looking at the diagnostic image, and roughly specify the extraction window so as to include the contour. Therefore, the operation becomes easy and the operation time can be shortened. Also, since the contour is extracted based on the pixel values of the pixels included in the extraction window,
Even if the extraction window is roughly specified, the contour can be extracted accurately and at high speed.

In the medical image diagnostic apparatus according to the second aspect, the operator selects one from a plurality of types of extraction windows.
You can select the one and adjust the size and position of the selected extraction window. Therefore, the operability is improved, and the extraction window can be specified more precisely.

In the medical image diagnostic apparatus according to the third aspect, the threshold range is determined based on the pixel values of the pixels included in the extraction window, and the binary image in the extraction window is obtained based on the threshold range. The contour of the region of interest is determined by the binary image. Since the processing is limited to the extraction window as described above, the contour can be extracted accurately and at high speed.

In the medical image diagnostic apparatus according to the fourth aspect, the first diagnostic image among a plurality of diagnostic images is displayed, and the operator is made to designate the extraction window including the region of interest. When the operator designates the extraction window, the contour of the region of interest is extracted from the first diagnostic image based on the pixel values of the pixels included in the extraction window. For the second and subsequent diagnostic images, the contours of the region of interest are extracted based on the contours extracted previously. Therefore, the operator may roughly estimate the contour of the region of interest while looking at the first diagnostic image, and roughly specify the extraction window so as to include the contour. Therefore, the operation becomes easy and the operation time can be shortened. Also, since the contour is extracted based on the pixel values of the pixels included in the extraction window, even if the extraction window is roughly specified,
The contour can be extracted accurately and at high speed. Also,
For the second and subsequent diagnostic images, the contour is extracted by using the contour extracted previously, so that the accuracy can be improved.

[0013]

The present invention will be described in more detail with reference to the embodiments shown in the drawings. The present invention is not limited to this.

First Embodiment FIG. 1 shows a medical image diagnostic apparatus 10 according to the first embodiment of the present invention.
It is a configuration block diagram of 0. This medical image diagnostic apparatus 10
Reference numeral 0 denotes the image pickup device 1, the image processing device 2, and the storage device 3.
, CRT 4, and input device 5.

The image pickup apparatus 1 is an X-ray CT apparatus or MRI.
It is an apparatus or an ultrasonic diagnostic apparatus, and passes a plurality of diagnostic images obtained by scanning the subject to the image processing apparatus 2.

The image processing apparatus 2 includes a control unit 2a, an extraction window selection unit 2b, and an extraction window adjustment unit 2c.
And a threshold value range determination unit 2d, a binarization processing unit 2e, and a contour determination unit 2f. The control unit 2a receives the diagnostic image from the imaging device 1 and stores the diagnostic image in the storage device 3. The extraction window selection unit 2b allows the operator to select one of a plurality of types of extraction windows (described later) prepared in advance. The extraction window adjustment unit 2c allows the operator to adjust the size and position of the extraction window.

The threshold value range determining unit 2d determines a threshold value from the pixel values of the pixels in the extraction window and sends the threshold value to the binarization processing unit 2e. The binarization processing unit 2e makes a binary image in the extraction window using the threshold value, and passes the binary image to the contour determining unit 2f. The contour determining unit 2f
A contour is extracted from the binary image.

FIG. 2 is a flow chart showing a contour extracting process in the medical image diagnostic apparatus 100. In step S1, N diagnostic images to be subjected to contour extraction processing are read in accordance with a command from the operator. Here, it is assumed that the N diagnostic images are time phase images at N time points in one cycle of the motion of the region of interest. In step S2, the number counter i is initialized to "1". In step S3, as shown in FIG.
As shown in, the i-th diagnostic image Gi is displayed on the CRT 4. Ki in the figure is a region of interest, such as the left ventricle of the heart. In step S4, the operator selects one of the extraction windows w1 to w4 shown in FIG. The extraction window w1 is rectangular, the extraction window w2 is elliptical or circular, and the extraction window w3 is
Is a triangle. The extraction window w4 is an extraction window of an arbitrary shape (the operator writes by hand using the input device 5). Here, a rectangular extraction window w
It is assumed that 1 is selected.

In step S5, as shown in FIG. 4, the extraction window w1 is displayed so as to be superimposed on the i-th diagnostic image Gi. In steps S6 and S7, the operator adjusts the size, direction, and position of the extraction window w1 so that the extraction window w1 includes the region of interest Ki, as shown in FIG. If the region of interest Ki expands and contracts like a heart, it is necessary to adjust the extraction window w1 assuming the maximum size.

In step S8, as shown in FIG. 6, a histogram Hi of pixel values of pixels in the extraction window w1 of the i-th diagnostic image is obtained, and a threshold range [a1, a2] is obtained based on the histogram Hi. At this time, since the processing range is limited in the extraction window w1, the threshold range can be accurately determined. On the other hand, if the extraction window is not set, as shown in FIG.
i ′ becomes complicated, and it becomes difficult to accurately determine the threshold range. Regarding the threshold value determination method, the simplest is to display the histogram Hi on the screen, and the operator may specify the lower threshold value a1 and the upper threshold value a2 with a mouse or the like.
Further, it can be automatically extracted by using any of a number of known threshold value determination methods (for example, described in "Medical Image Processing: Shigeru Eiho: Asakura Shoten: Pages 68 to 70"). Existing threshold determination method can be used).

In step S9, the pixel value is in the threshold range [a
1, a2], the pixel value is set to “1”.
And the pixel value of the pixel whose pixel value does not belong to the threshold range [a1, a2] is converted to “0”, and the extraction window w1 is converted into a binary image. In step S10,
For example, the 8-point tracking method (described later) is applied to the binary image, the i-th contour is extracted, and the i-th contour is subjected to correction such as smoothing (described later) to obtain the i-th contour. decide. In the 8-point tracking method, a binary image is scanned to obtain a point on the contour (for example, a point having a pixel value = 1), the point is set as a tracking start point, and 8 points adjacent to the tracking start point are rotated clockwise. The points on the next contour (for example, the point having the pixel value = 1) are searched in order. By repeating this and returning to the contour start point, the contour is obtained. As the smoothing correction, for example, thinning and Laplace interpolation are performed. The thinning is a process of inspecting a contour and, if the contour is thick, cutting the contour from the outside to evenly adjust the thickness of the contour. The Laplace interpolation is a process of correcting contours so that the contours are smoothly connected.

At step S11, as shown in FIG.
The i-th contour Ri is displayed so as to be superimposed on the i-th diagnostic image Gi. In step S12, the operator is prompted to input whether or not the displayed i-th contour Ri is satisfactory. If "unsatisfied" is input, the process is repeated from step S3. When "satisfaction" is input, the process proceeds to step S13.

In steps S13 and S14, the steps from the second diagnostic image to the (N-1) th diagnostic image are performed in step S3.
~ Repeat S12.

At step S15, as shown in FIG.
The (N-1) th contour RN-1 and the first contour R1 are used as clues to extract the Nth contour RN from the Nth diagnostic image, and correction such as smoothing is performed to determine the Nth contour RN. In particular,
By applying the above-mentioned 8-point tracking method, the Nth contour R in the Nth diagnostic image
N is extracted, and the area, circumference, circularity of the Nth contour RN,
Find characteristic parameters such as the center of gravity. In addition, the (N-
1) Feature parameters are also obtained for the contour RN-1 and the first contour R1. Then, the Nth contour R is arranged such that the feature parameter of the Nth contour RN is inserted between the feature parameter of the (N-1) th contour RN-1 and the feature parameter of the first contour R1.
Fix N. As a result, the contour of the region of interest is smoothly connected over one cycle of the motion of the region of interest.

In the above-mentioned medical image diagnostic apparatus 100, the operator roughly estimates the contour of the region of interest Ki while looking at the diagnostic image Gi, and selects one of a plurality of types of extraction windows so as to include the contour. Select one, adjust its size and position, and the contour will be extracted automatically. Therefore, the operation becomes easy and the operation time can be shortened. Further, since the contour is extracted based on the pixel values of the pixels included in the extraction window, the contour can be accurately and rapidly extracted even if the extraction window is roughly specified.

-Second Embodiment- The block diagram of the medical image diagnostic apparatus according to the second embodiment of the present invention is the same as that shown in FIG. FIG. 10 is a flowchart showing the contour extraction processing in the medical image diagnostic apparatus of the second embodiment. In step V1, N diagnostic images to be subjected to the contour extraction processing are read in accordance with an instruction from the operator. Here, it is assumed that the N diagnostic images are time phase images at N time points in one cycle of the motion of the region of interest. Step V
In 2, the first diagnostic image is displayed on the CRT 4. In step V3, the operator selects one of the plurality of types of extraction windows.

In step V4, the extraction window is set to the first
Displayed on the diagnostic image. In steps V5 and V6,
Let the operator adjust the size, orientation and position of the extraction window so that it contains the region of interest. In step V7, the threshold value is obtained based on the pixel value of the pixel in the extraction window of the first diagnostic image. In step V8,
A binary image is created in the extraction window based on the threshold range. In step V9, the first contour is extracted from the binary image, and the first contour is determined by performing correction such as smoothing on the first contour.

At step V10, the number counter i is initialized to "2". In step V11, the (i-
1) The i-th contour is extracted from the i-th diagnostic image using the contour as a clue, and correction such as smoothing is performed to determine the i-th contour.
In steps V12 and V13, the (Nth)
-1) Repeat step V11 for the diagnostic image.

In step V14, the (N-1) th contour and the first contour are used as clues to extract the Nth contour from the Nth diagnostic image, and correction such as smoothing is performed to determine the Nth contour. Specifically, the above-mentioned 8-point tracking method is applied to extract the Nth contour RN from the Nth diagnostic image, and the characteristic parameters such as the area, circumference, circularity, and center of gravity of the Nth contour RN are obtained. Further, the characteristic parameter is also obtained for the (N-1) th contour RN-1. Then, the N-th contour RN is modified so that the feature parameter of the (N-1) th contour RN-1 and the feature parameter of the N-th contour RN are not separated from each other. Further smoothing is performed. As a result, the contour of the region of interest is smoothly connected over one cycle of the motion of the region of interest.

In the medical image diagnostic apparatus of the second embodiment described above, if the operator specifies the extraction window only for the first diagnostic image, the contour will be automatically extracted. Therefore,
The operation becomes easier and the operation time can be shortened. Also,
Since the contour is extracted based on the pixel value of the pixel included in the extraction window, the contour can be accurately and rapidly extracted even if the designation of the extraction window is rough. Further, for the second and later diagnostic images, the contour is extracted using the contour extracted previously, so that the accuracy can be improved.

[0031]

According to the medical image diagnostic apparatus of the present invention,
If the operator roughly estimates the contour of the region of interest while looking at the diagnostic image and roughly specifies the extraction window so as to include the contour, the contour is automatically extracted. Therefore, the operation becomes easy and the operation time can be shortened. Also,
Since the contour is extracted based on the pixel values of the pixels included in the extraction window, the contour can be accurately and rapidly extracted even if the extraction window is roughly specified.

[Brief description of drawings]

FIG. 1 is a configuration block diagram of a medical image diagnostic apparatus according to a first embodiment of the present invention.

FIG. 2 is a flowchart of contour extraction processing according to the first embodiment.

FIG. 3 is an explanatory diagram of selection of an extraction window.

FIG. 4 is an explanatory diagram of display of an extraction window.

FIG. 5 is an explanatory diagram of adjustment of an extraction window.

FIG. 6 is an exemplary diagram of a histogram of pixels in an extraction window.

FIG. 7 is an exemplary diagram of a histogram of pixels of the entire diagnostic image.

FIG. 8 is an explanatory diagram of contour display.

FIG. 9 is an explanatory diagram of contour extraction using the contour extracted previously.

FIG. 10 is a flowchart of contour extraction processing according to the second embodiment.

FIG. 11 is a flowchart of an example of conventional contour extraction processing.

FIG. 12 is a view showing an example of a first diagnostic image.

FIG. 13 is an explanatory diagram of designation of input points.

FIG. 14 is an explanatory diagram of a center point and scanning lines.

FIG. 15 is an explanatory diagram of contour points.

FIG. 16 is a view showing an example of a first contour.

[Explanation of symbols]

 100 medical image diagnostic device 1 imaging device 2 image processing device 2a control unit 2b extraction window selection unit 2c extraction window adjustment unit 2d threshold value range determination unit 2e binarization processing unit 2f contour determination unit G1, Gi diagnostic image K1, Ki region of interest R1, Ri contour w1 to w4 extraction window

Claims (4)

[Claims] 1. A medical image diagnostic apparatus having a contour extracting function for extracting a contour of a region of interest in a diagnostic image, the extraction window designating unit displaying a diagnostic image and allowing an operator to designate an extraction window including the region of interest. And a contour extraction unit that extracts a contour of the region of interest based on a pixel value of a pixel included in the extraction window. 2. The medical image diagnostic apparatus according to claim 1, wherein the extraction window designating means allows the operator to select one of a plurality of types of extraction windows, and the selected extraction window. A medical image diagnostic apparatus, comprising: an extraction window adjusting means that allows an operator to adjust the size and position of the window. 3. The medical image diagnostic apparatus according to claim 1 or 2, wherein the contour extraction means determines a threshold range based on pixel values of pixels included in the extraction window. A binary processing unit for obtaining a binary image in the extraction window based on the threshold range; and a contour determining unit for determining the contour of the region of interest based on the binary image. Image diagnostic equipment. 4. The medical image diagnostic apparatus according to any one of claims 1 to 3, wherein the extraction window designating means is a plurality of diagnostic images obtained by imaging the same target at different times or different close to the same target. The operator selects the extraction window including the region of interest by displaying the first diagnostic image among the plurality of diagnostic images captured by the region, and the contour extracting unit first determines the pixel included in the extraction window for the first diagnostic image. A medical image diagnostic apparatus, characterized in that the contour of the region of interest is extracted based on pixel values, and then the contours of the region of interest are respectively extracted based on the contours previously extracted for the second and subsequent diagnostic images.