JPH0767446B2 - CT image contour extraction method - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a lesion contour extraction method, and in particular, in cancer radiotherapy, a cancer lesion contour that serves as a basis for determining radiation irradiation when planning an optimal radiation irradiation method ( Target).

2. Description of the Related Art Conventionally, this kind of cancer lesion contour (target) is determined by a doctor who is an operator manually inputting a series of CT (Computer Tomography) images related to each other with a graphic pen or the like. Is the current situation.
Therefore, in addition to being extremely complicated, there is a drawback that some errors occur due to manual input.

OBJECTS OF THE INVENTION Therefore, the present invention has been made to solve the drawbacks of the conventional ones, and an object thereof is to automatically determine a cancer lesion contour (target) without complexity and error. It is to provide a method of extracting a lesion contour of a CT image that is performed.

According to the present invention, there is provided a lesion contour extraction method for extracting contours of cancer lesions in a plurality of CT images which are related to each other, wherein one of the contours of cancer lesions in one CT image in which cancer is most prominent is detected. Input means for designating a point and the CT value of the one point are used as a threshold value to perform binarization processing for all pixels of this CT image.
There are provided a binarizing unit, a contour extracting unit that traces and extracts the contour of the cancer lesion using the result of the binarization with the one point as a starting point, and a centroid calculating unit that obtains the barycentric position of the cancer lesion. A lesion contour characterized by performing each processing by the binarizing means, contour extracting means, and centroid calculating means while using the CT value and position of the centroid as a threshold value and a contour tracking start point of the next CT image. The extraction method is obtained.

Embodiment Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a system block diagram of an embodiment of the present invention.
In the figure, the input device 1 is a transmissive digitizer,
A manual input operation is performed using the graphic pin. In the processing of the present embodiment, an operation of designating and inputting one point in the contour of a cancer lesion and an operation of inputting an isocenter (corresponding to the center of gravity of the cancer lesion) on the CT image in which the cancer is most prominent are performed. Done.

The lesion contour extraction mechanism 2 is a functional part that is the gist of the present invention, and includes the following four functional blocks. The CT value / coordinate reading unit 21 detects the designated one point in the contour of the cancer lesion with respect to the CT image in which the cancer is most prominently input from the input device 1.
It has the function of reading CT values and their coordinate values (xy coordinate values). Here, the CT value is information on the subject (human body) obtained when the subject (human body) is imaged by a CT scanner, and is a value representing the X-ray absorption rate of the subject.

The binarization unit 22 has a function of performing binarization processing on all pixels (all pixels) of this CT image using the designated CT value of one point as a threshold value.

The boundary extraction unit 23 sets the designated one point as a start point,
It has a function of tracing and extracting the contour of a cancer lesion while using the binarization processing result. The focus centroid calculation unit 24 has a function of obtaining the centroid position of a cancer lesion.

The CT image file 3 is a file in which a series of CT images related to each other is stored in advance. A CT image required for a treatment plan is stored in the hard disk, and when it is needed for processing, it is read out to a memory (not shown) and used for each processing and output.

The output device 4 is a graphic display and displays a CT image. An input operation is performed by the input device 1 while looking at the CT image displayed on this display.

FIG. 2 is a flow chart showing the operation of the blocks of FIG. First, in steps 200 and 201, for a CT image in which cancer is most prominent (this image is referred to as CT image No. n), only one point in the contour of the cancer lesion is the graphic of the input device 1. It is specified and input with a pen. The coordinates (x ₀ , y ₀ ) of this designated one point and CT _a , which is the CT value, are read.

Next, in step 202, binarization processing is performed on all the pixels of the CT image No. n using the CT _a value as a threshold value. That is, the binarization processing is performed by setting “1” when the CT value of each pixel is equal to or larger than the CT _a value and “0” in other cases. This is due to the fact that the CT value of cancerous tissue is generally slightly higher than that of surrounding tissue.

In the next step 203, the boundary of the cancer lesion is traced starting from the coordinate (x ₀ , y ₀ ) of the designated one point. This boundary tracking can be performed by a method using the well-known 3 × 3 mask method. For details of this 3 × 3 mask method, refer to “Computer Image Processing” PP.65-66,
It has been disclosed in Takeshi Yasuiin and Masayuki Nakajima, published by the industry newsletter.

In step 204, the center of gravity of the lesion obtained in the above step is obtained. The reason for obtaining the center of gravity is as follows. In the above steps, CT image N
The cancer lesion contour (target) of “on” was obtained, but after that, it is necessary to obtain the contour of the cancer lesion for all CT images of No. n + 1 or more and No. n−1 or earlier. Therefore, the doctor's one-point input is not required thereafter, and the contours of cancer lesions in each CT image are automatically obtained one after another. However, in two adjacent CT images, the isocenter (center of gravity) is more The coordinates of the center of gravity of the lesion in the near CT image are the other CT
The center of gravity is obtained by utilizing the fact that the image is highly likely to be included in the lesion.

3 and 4 are diagrams for explaining how to obtain the center of gravity. Among the points on the lesion contour, the points having the maximum values x _max and y _max at the x and y coordinates and the points having the minimum points x _min and y _min are obtained, respectively. Then, the temporary center of gravity P (x _c , y _c ) is _calculated by the following equation.

x _c = (x _max −x _min ) / 2 y _c = (y _max −y _min ) / 2 It is necessary to confirm that the temporary barycentric position P thus obtained is within the lesion contour. If not within
It is necessary to find a point within the contour and close to the center of gravity.

Therefore, first, an algorithm for determining that it is within the lesion contour will be described with reference to FIGS. 3 (a) and 3 (b). Starting from the temporary center of gravity P obtained above, a half line is drawn in an arbitrary direction, and all intersection points with the contour are obtained. Whether the contour is inside or outside can be determined based on whether the number of intersections at this time is an even number or an odd number.

If the tentative center of gravity P is inside the contour as shown in FIG. 3 (A), the number of intersections is odd, and if outside the contour as shown in FIG. 3 (B), it is even.

When it is determined that the provisional center of gravity P is outside the contour by such a method, the center of gravity P ′ is obtained by the following method. It is obvious that the provisional center of gravity P becomes the desired center of gravity when it is determined that it is within the contour.

FIG. 4 is an explanatory diagram for obtaining the center of gravity P '. Starting from the temporary center of gravity P, the positive x direction, negative x direction, positive y direction, and y
Extend half lines 41-44 in each of the negative directions. These half lines 41
In the order in which ~ 44 intersects the contour, the points x _i1 , x _i2 ,
Numbers are added to the intersections with x _i3 , ... (i is a half line 41-44
Indicates the lower digit number).

Then, the number i having the maximum distance between the points x _i1 and x _i2 is obtained. In the example of FIG. 4, i = 3 (y positive direction). The midpoint between these points x ₃₁ and x ₃₂ is the center of gravity P ′ to be obtained. This center of gravity P ′ does not necessarily have to be the geometric center of gravity of the lesion,
It suffices if the following one condition is satisfied. I.e. adjacent
In the CT image, the condition is that a point having the same x and y coordinates as the center of gravity P ′ of this CT image is within the lesion outline.

Next, the same processing as described above is performed for the No. n + 1 CT image, but binarization processing of all pixels is performed in step 202, and in the next step 203, the barycentric position of the No. n CT image is set to the start point. As a result, boundary tracking processing using the 3 × 3 mask method is performed.

However, in rare cases, it is possible to extract contours other than the lesion, so in this case,
The doctor specifies which contour is the lesion contour. Then, the lesion centroid of the No. n + 1 CT image is obtained in step 204.

After that, similarly, the process automatically proceeds for each CT image of No. n + 2 and later, and when the doctor determines that there is no cancer tissue after a certain CT image, a stop command is issued. CT in response to this
The cancer lesion contour extraction in the increasing direction of the slice number is completed (step 205).

Similarly, for No.n−1, No.n−2, ... CT images, step
The processes of 202, 203, and 204 are repeated, and the cancer lesion contour extraction in the CT slice No. 1 direction ends in response to the doctor's stop command (step 206).

EFFECTS OF THE INVENTION As described above, according to the present invention, based on the CT value and the coordinate of one point on the lesion contour designated by the doctor, the boundary tracking processing of all CT slice images is automatically performed thereafter. Since it is performed, there is an effect that complexity can be eliminated and an error due to acquisition can be eliminated.

[Brief description of drawings]

FIG. 1 is a system block diagram of an embodiment of the present invention, FIG. 2 is an operation flowchart of the embodiment of the present invention, and FIGS. 3 and 4 are diagrams for explaining an algorithm for obtaining a center of gravity. Description of main part code 21 …… CT value / coordinate reading unit 22 …… Binarization unit 23 …… Boundary extraction unit 24 …… Focal center of gravity calculation unit

Claims (1)

[Claims] 1. A lesion contour extraction method for extracting contours of cancer lesions in a plurality of CT images associated with each other, wherein one point in the contours of cancer lesions in one CT image in which cancer is most prominent is designated. Using the input means, the binarization means for binarizing all the pixels of this CT image with the CT value of the one point as the threshold value, and the binarization result of the one point as the starting point A contour extracting means for tracing and extracting the contour of the cancer lesion, and a centroid calculating means for determining the centroid position of the cancer lesion are provided, and the CT value and position of the centroid are used as the threshold value and contour tracing starting point of the next CT image. Meanwhile, a lesion contour extraction method characterized in that each processing by the binarization means, contour extraction means, and center of gravity calculation means is performed.