JP2003250794A - Tumor region-detecting method and x-ray ct apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To highly accurately detect a tumor region. <P>SOLUTION: The X-ray CT apparatus 100 is equipped with a scanner apparatus 1, a processing apparatus 2, a large capacity storage apparatus 3, a display monitor 4, and an inputting apparatus 5. The processing apparatus 2 is equipped with a data-obtaining unit 2a, a blood vessel candidate region-extracting unit 2b, a blood vessel region-extracting unit 2c, a tumor candidate region-extracting unit 2d, and a tumor region-detecting unit 2e. In this case, the data-obtaining unit 2a inputs a plurality of continuous CT tomographic images which are obtained by performing an X-ray scanning for a subject at different tomographic locations. The blood vessel candidate region-extracting unit 2b extracts blood vessel candidate regions in a plurality of the continuous CT tomographic images based on CT values. The blood vessel region-extracting unit 2c determines a region wherein the volume of an external contact rectangular parallelepiped is larger as a blood vessel region from among blood vessel candidate regions. The tumor candidate region-extracting unit 2d applies a contracting process to the blood vessel region, and determines an isolated region as a tumor candidate region. The tumor region-detecting unit 2e detects a tumor region based on three-dimensional characteristics of the tumor candidate region. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for detecting a tumor region and an X-ray CT apparatus, and more specifically, it processes and measures a CT tomographic image (including an axial scan tomographic image and a helical scan tomographic image) to measure a tumor. The present invention relates to a tumor area detection method and an X-ray CT apparatus that detect an area with high accuracy.

[0002]

2. Description of the Related Art Conventionally, a tumor area in a lung field area is detected as follows. First, as shown in FIG. 14, the subject h is X-ray scanned to obtain a CT tomographic image Ai of the lung L.

Next, as shown in FIG. 15, the range of CT values including the tumor area is set to "1", and the others are set to "0".
Binarization processing is performed. And the area | region of "1" is made into the tumor candidate area Ca. The CT value range for binarization is
Since the tumor candidate region Ca includes not only the tumor region but also the blood vessel region, not only the tumor region but also the blood vessel region is included. Here, since the tumor has a substantially spherical shape, the sectional shape is circular. On the other hand, since the blood vessel often travels so as to cross the axial cross section at an angle, the cross-sectional shape on the CT tomographic image Ai is often an ellipse. Therefore, the circularity α of each tumor candidate region Ca is calculated, and the circularity α is “1”.
A tumor candidate region Ca close to is determined to be a tumor candidate. In addition,
As is generally known, the circularity α is α = 4 · π · S / L ^2, where S is the area of the tumor candidate region Ca and L is the perimeter. However, 0 <α ≦ 1.

[0004]

However, when the blood vessel is running so as to be orthogonal to the axial section,
The circularity α of the blood vessel region is also close to “1”. Therefore, the tumor area and the blood vessel area are mistaken. That is, the conventional tumor area detection method has a problem that the accuracy of detecting a tumor area is low. Therefore, an object of the present invention is to provide a tumor area detecting method and an X-ray CT apparatus capable of detecting a tumor area with high accuracy by image-processing and measuring a CT image.

[0005]

According to a first aspect of the present invention, according to the present invention, a plurality of CT tomographic images obtained by X-ray scanning a subject at consecutive different tomographic image positions are image-processed and measured to obtain a tumor candidate. Provided is a tumor area detection method characterized by detecting a tumor area based on a three-dimensional shape feature parameter of the area.
In the tumor area detection method according to the first aspect, the tumor area is not detected by the circularity (two-dimensional feature) of the tumor candidate area in one CT tomographic image as in the prior art, but a plurality of consecutive CTs are detected. The tumor region is detected based on the three-dimensional shape feature of the tumor candidate region in the tomographic image. When viewed three-dimensionally, the tumor has a substantially spherical shape, but since the blood vessel has a columnar shape, it is easy to distinguish the tumor even from the blood vessel running so as to be orthogonal to the axial cross section. Therefore, the tumor area can be detected with high accuracy.

In a second aspect, the present invention provides a tumor area detecting method having the above-mentioned structure, wherein the three-dimensional shape characteristic parameter includes at least sphericity. In the tumor area detecting method according to the second aspect, at least the sphericity is used as the three-dimensional shape feature parameter. Here, the sphericity β or β ′ is
When the surface area is S and the volume is V, β = 6 · π ^1/2 · V / S ^3/2 β ′ = β ² = 36 · π · V ² / S ³ . The closer the sphericity β or β ′ is to “1”, the higher the probability of being a tumor.

According to a third aspect of the present invention, in the method for detecting a tumor area having the above-mentioned structure, the average CT value of the candidate tumor areas is also used as a criterion for determining whether or not the area is a tumor area. I will provide a. In the tumor area detection method according to the third aspect, whether or not the tumor is a tumor area is also determined using the average CT value of the tumor area candidates. The detection accuracy can be improved by using a plurality of judgment criteria.

According to a fourth aspect of the present invention, in the method for detecting a tumor region having the above-mentioned structure, a candidate blood vessel region in a plurality of CT tomographic images acquired by X-ray scanning an object at consecutive different tomographic image positions is selected. A region having a large volume of a circumscribed rectangular parallelepiped is extracted as a blood vessel region in the blood vessel candidate region from the CT value to distinguish it from a noise region, and a contraction process is performed on the blood vessel region to isolate an isolated region as a tumor candidate region. A method for detecting a tumor area is provided. Tumors have the following properties. (1) Tumors are connected to blood vessels for nutrition. (2) The tumor becomes thicker than the blood vessels connecting to itself. Therefore, in the tumor area detection method according to the fourth aspect,
First, when a blood vessel region is extracted, a tumor is included in this blood vessel region because of the above property (1). Next, when the blood vessel is contracted by the thickness of the blood vessel, the tumor remains isolated due to the property (2). Thus, the tumor candidate region can be extracted.

According to a fifth aspect of the present invention, in the method for detecting a tumor region having the above-mentioned structure, a threshold level for tumor detection is provided for each of two or more regions, and the contraction process is performed separately for each tumor. An area detection method is provided. Even if we say that only the thickness of blood vessels is contracted, the thickness of blood vessels is not uniform. That is, it is thick at the trunk and thin at the periphery. Therefore, in the tumor area detecting method according to the fifth aspect, for example, a threshold level for tumor detection is set for each of two or more areas near the main and distal ends of a blood vessel, and contraction processing is performed separately. . As a result, the tumor of the main part and the tumor of the peripheral part can be detected with less oversight and overdetection.

According to a sixth aspect of the present invention, in the method for detecting a tumor area having the above-mentioned structure, a lung field portion is detected from a plurality of axial CT images based on a CT value, and thereafter, in the lung field portion. A method for detecting a tumor region, which is characterized by extracting a blood vessel candidate region based on a CT value. In the tumor area detecting method according to the sixth aspect, first, the lung field portion is detected and the blood vessel candidate area is extracted within the lung field portion, so that the noise area can be suitably removed.

According to a seventh aspect of the present invention, in the method for detecting a tumor region having the above-mentioned structure, a blood vessel candidate region in a region in which a blood vessel portion in a lung field portion is filled and shaped is extracted based on a CT value. A method for detecting a tumor region is provided. In the tumor area detection method according to the seventh aspect, since the blood vessel candidate area is extracted after shaping the blood vessel portion in the lung field portion, the blood vessel candidate area can be suitably extracted.

According to an eighth aspect of the present invention, in the method for detecting a tumor region having the above-mentioned structure, a blood vessel candidate region within a region formed by three-dimensional image processing of a lung field portion is extracted based on a CT value. A method for detecting a tumor area is provided. In the tumor area detecting method according to the eighth aspect, since the blood vessel candidate area is extracted after performing the shaping by the three-dimensional image processing in the lung field portion, the blood vessel candidate area can be suitably extracted.

According to a ninth aspect, the present invention provides a photographing means for X-ray scanning an object at consecutive different tomographic image positions to obtain a plurality of CT tomographic images, and image processing of the plurality of CT tomographic images.
There is provided an X-ray CT apparatus characterized by comprising a tumor region detecting means for measuring and detecting a tumor region based on a three-dimensional shape characteristic parameter of a tumor candidate region. In the X-ray CT apparatus according to the ninth aspect, the tumor area detecting method according to the first aspect can be suitably implemented.

According to a tenth aspect of the present invention, in the X-ray CT apparatus having the above structure, the three-dimensional shape feature parameter is
An X-ray CT apparatus including at least a sphericity is provided. The X-ray CT apparatus according to the tenth aspect can suitably implement the tumor area detection method according to the second aspect.

In an eleventh aspect, the present invention is an X-ray CT apparatus having the above-mentioned structure, wherein the average CT value of a tumor area candidate is also used as a criterion for determining whether or not a tumor area is present. I will provide a. X according to the eleventh aspect
In the line CT apparatus, the tumor area detecting method according to the third aspect can be suitably implemented.

In a twelfth aspect of the present invention, in the X-ray CT apparatus having the above-mentioned structure, a candidate blood vessel region in a plurality of CT tomographic images acquired by X-ray scanning a subject at consecutive different tomographic image positions is obtained. A blood vessel candidate region extracting means for extracting based on a CT value, a blood vessel region extracting means for making a region of the blood vessel candidate region having a large volume of a circumscribing rectangular parallelepiped a blood vessel region, and performing a contraction process on the blood vessel region. There is provided an X-ray CT apparatus characterized by comprising a tumor candidate area extracting unit that uses an isolated area as a tumor candidate area. The X-ray CT apparatus according to the twelfth aspect can suitably implement the tumor area detecting method according to the fourth aspect.

In a thirteenth aspect, the present invention is characterized in that, in the X-ray CT apparatus having the above-mentioned configuration, a threshold level for tumor detection is provided for each of two or more regions, and contraction processing is performed separately for each X. A line CT apparatus is provided. The X-ray CT apparatus according to the thirteenth aspect can suitably implement the tumor area detection method according to the fifth aspect.

According to a fourteenth aspect of the present invention, in the X-ray CT apparatus having the above structure, the lung field portion is detected from a plurality of axial CT images based on the CT value, and thereafter, in the lung field portion. There is provided an X-ray CT apparatus characterized by extracting a blood vessel candidate region based on a CT value. First above
The X-ray CT apparatus according to the fourth aspect can preferably implement the tumor area detection method according to the sixth aspect.

According to a fifteenth aspect of the present invention, in the X-ray CT apparatus having the above-mentioned configuration, a blood vessel candidate region in a region in which a blood vessel portion in the lung field portion is filled and shaped is extracted based on the CT value. An X-ray CT apparatus characterized by the above. The X-ray CT apparatus according to the fifteenth aspect can suitably implement the tumor area detecting method according to the seventh aspect.

In a sixteenth aspect, the present invention is, in the X-ray CT apparatus having the above-mentioned configuration, extracting a blood vessel candidate region within a region formed by three-dimensional image processing of a lung field portion based on a CT value. An X-ray CT apparatus is provided.
The X-ray CT apparatus according to the sixteenth aspect can suitably implement the tumor area detection method according to the eighth aspect.

[0021]

BEST MODE FOR CARRYING OUT THE INVENTION The present invention will now be described in more detail with reference to the embodiments of the present invention shown in the drawings. The present invention is not limited to this.

FIG. 1 is a block diagram showing an X-ray CT apparatus according to an embodiment of the present invention. This X-ray CT apparatus 100
Includes a scanner device 1, a processing device 2, a mass storage device 3, a display monitor 4, and an input device 5 such as a keyboard and a mouse.

The scanner device 1 moves the X-ray tube and the detector relative to the subject along the body axis of the subject, stops them at a desired tomographic image position, and moves the X-ray tube and the detector around the body axis. The instrument is rotated to collect data for multiple rotation views required to create a CT tomographic image. Note that there may be a fourth-generation X-ray CT apparatus in which only the X-ray tube is rotated and the detector is not rotated.

The processing apparatus 2 stores a data acquisition unit 2a for inputting a plurality of CT tomographic images obtained by scanning the subject at consecutive different tomographic image positions by X-rays, and a blood vessel candidate region in the plurality of CT tomographic images. A blood vessel candidate region extraction unit 2b that extracts based on the CT value, a blood vessel region extraction unit 2c that uses a region of the blood vessel candidate region having a large volume of a circumscribed rectangular parallelepiped as a blood vessel region, and performs contraction processing on the blood vessel region. A tumor candidate area extraction unit 2d that uses an isolated area as a tumor candidate area, and a tumor candidate area 3
A tumor area detection unit 2e for detecting a tumor area based on the three-dimensional shape characteristic parameter is provided.

FIG. 2 is a flowchart showing the tumor area detection processing by the X-ray CT apparatus 100. Note that the lung is assumed as the organ for tumor detection. In step P1,
As shown in FIG. 3, a plurality of CT tomographic images Si acquired by X-ray scanning the subject h at different continuous tomographic image positions are input.

In step P2, a binarization process is performed so that the CT value range (for example, -200 or less) capable of extracting the entire lung is set to "1" and the other values are set to "0".
As a result, as shown in FIG. 4, the area of the lung L and the background (air) A becomes "1", and the other areas become "0". In FIG. 4, the area of “1” is hatched. In FIG. 4, the inlet La is a thick blood vessel portion that enters the lung L.

At step P3, 3 is added to the area of "1".
N-pixel contraction processing and N-pixel expansion processing are performed by a two-dimensional or two-dimensional logical filter to remove a minute area that becomes noise (for example, N = 5. However, the value of N is changed depending on one pixel size. ). Next, the area of "1" is divided by the three-dimensional or two-dimensional labeling processing, and the division that can be estimated as the background (air) is removed. As shown in FIG. 5, only the lung becomes the area of “1”.

At step P4, 3 is added to the area of "1".
M pixel expansion processing and M pixel contraction processing are performed by a two-dimensional or two-dimensional logical filter, and an unnecessary area La
(For example, M = 10. However, the value of M is changed depending on the size of one pixel). As shown in FIG.
The inlet La disappears from the lung L.

In step P5, the area which has become "1" in step P4 is cut out from each CT tomographic image Si by the AND operation of the inter-pixel operations, and the blood vessel can be extracted from the cut out area (mask area). CT value range (CT value of contrasted blood vessel is about 100 to 20)
Since the CT value of a blood vessel that is not 0 and is not contrasted is approximately 40 to 50, the binarization processing is performed so that, for example, −500 to +1000) is set to “1” and the other values are set to “0”. As a result, as shown in FIG. 7, the blood vessel candidate region Va of the lung L becomes "1" and the other regions become "0". Where the lung L
The blood vessel candidate region Va of 3 is modeled three-dimensionally.

In step P6, the blood vessel candidate region V of the lung L
L pixel expansion processing and L pixel contraction processing are performed on a by a three-dimensional logical filter, and the blood vessel candidate regions v1, v2, v3, ... Which have been cut off in the data are connected and repaired as shown in FIG. 8 (for example, L = 1 to 3. However, the value of L is changed depending on the one pixel size.).

In step P7, the blood vessel candidate region Va is converted into the blood vessel candidate V as shown in FIG. 9 by the three-dimensional labeling process.
Divide into b.

At step P8, as shown in FIG. 9, xm including all pixels of the blood vessel candidate region for each blood vessel candidate Vb
in <x <xmax and ymin <y <ymax and zmin <z
The volume of the circumscribed rectangular parallelepiped B, which is a rectangular parallelepiped region of <zmax, is calculated, and the blood vessel candidate Vb having the largest volume is determined as the blood vessel V. Other label areas are removed as noise areas. FIG. 10 shows the blood vessel V. In FIG. 9, the blood vessel candidates Vb are the upper surface AP, the bottom surface BP, and the side surface CP of the circumscribed rectangular parallelepiped B.
To FP at points Ca to Cf

In step P9, P-pixel contraction processing is performed on the blood vessel V by a three-dimensional logical filter to erase the blood vessel in the peripheral portion. Let P be a value obtained by adding 1 to 2 pixels to the thickness of the peripheral blood vessel (for example, P = 4 to 5; however, the value of P is changed depending on the 1 pixel size). As a result, as shown in FIG. 11, the isolated tumor candidate region Ca remains in the peripheral portion of the blood vessel V. Next, the sphericity β of the tumor candidate area Ca is calculated. Also, three-dimensional data of the tumor candidate region Ca is extracted based on the tumor candidate region Ca and the plurality of continuous CT tomographic images Si, and the average CT value thereof is calculated. And
The peripheral tumor region is detected based on the sphericity β of the tumor candidate region Ca and the average CT value. Here, the tumor C has a substantially spherical shape as shown in FIG. 12 (a), and the blood vessel V has a substantially columnar shape as shown in FIG. 12 (b). Therefore, for example, if the sphericity β is larger than 0.8 and the average CT value is 0 to +200, it is determined to be a tumor region, and if not, it is determined to be not a tumor region.

In step P10, Q-pixel contraction processing is performed on the blood vessel V remaining after the contraction processing after step P9 by a three-dimensional logical filter to erase the blood vessel in the main trunk. Q is 1 to 2 pixels added to the thickness of the blood vessel of the main trunk P
Is subtracted (for example, Q = 7 to 8. However, the value of Q is changed depending on the size of one pixel). As a result, as shown in FIG. 13, an isolated tumor candidate region Cb remains in the main trunk of the blood vessel V. Next, the tumor candidate region Cb
Calculate the sphericity β of. Also, three-dimensional data of the tumor candidate region Cb is extracted based on the tumor candidate region Cb and the plurality of continuous CT tomographic images Si, and the average CT value thereof is calculated. Then, the main trunk tumor region is detected based on the sphericity β of the tumor candidate region Cb and the average CT value.

As described above, the tumor area can be detected with high accuracy.

In the above description, the processing up to step P5 is a two-dimensional processing and the processing after step P6 is a three-dimensional processing, but all the processing after step P2 may be a three-dimensional processing.
In this case, in step P1, a plurality of CT tomographic images Si
3D data should be constructed from.

Further, in the above description, the blood vessel is divided into the peripheral portion and the main portion in two stages of the thickness, and the detection threshold of the tumor region is set. However, the region of the blood vessel is divided into the thicknesses of three stages or more and the tumor region is divided. May be detected.

The step P6 may be omitted.

[0039]

INDUSTRIAL APPLICABILITY The tumor area detecting method and X-ray C of the present invention
The T-apparatus can detect the tumor area with high accuracy.

[Brief description of drawings]

FIG. 1 is a configuration diagram of an X-ray CT apparatus according to an embodiment of the present invention.

FIG. 2 is a flowchart showing a tumor area detection process according to the embodiment of the present invention.

FIG. 3 is an exemplary view of a plurality of CT tomographic images.

FIG. 4 is an explanatory diagram of binarization processing for extracting the entire lung.

FIG. 5 is an explanatory diagram of a process of deleting a background.

FIG. 6 is an explanatory diagram of a process of filling an unnecessary area.

FIG. 7 is an explanatory diagram of blood vessel candidate regions.

FIG. 8 is an explanatory diagram of connection repair processing of a blood vessel candidate region.

FIG. 9 is an explanatory diagram showing a rectangular parallelepiped circumscribing a blood vessel candidate.

FIG. 10 is an explanatory diagram of extracted blood vessels.

FIG. 11 is an explanatory diagram of a peripheral tumor candidate region.

FIG. 12 is an explanatory diagram showing a difference in shape between a tumor and a blood vessel.

FIG. 13 is an explanatory diagram of a tumor candidate region of the main trunk.

FIG. 14 is a view showing an example of a CT tomographic image.

FIG. 15 is an explanatory diagram of a conventional tumor candidate area.

[Explanation of symbols]

100 X-ray CT system 1 Scanner device 2 processing equipment 2a Data acquisition unit 2b Blood vessel candidate area extraction unit 2c blood vessel region extraction unit 2d Tumor candidate region extraction unit 2e Tumor area detector 3 Mass storage 4 Display monitor 5 Input device

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Akihiko Nishide             127, 4-7 Asahigaoka, Hino City, Tokyo             GE Yokogawa Medical System Co., Ltd.             Within (72) Inventor Yasuhiro Imai             127, 4-7 Asahigaoka, Hino City, Tokyo             GE Yokogawa Medical System Co., Ltd.             Within F term (reference) 4C093 AA22 DA02 DA10 FD03 FD05                       FD09 FF17 FF19 FF20 FF21                       FF42                 5B057 AA09 BA03 CA13 CB13 CH01                       CH12 DA08 DA16 DB03 DC16                 5L096 AA09 BA06 BA13 DA01 EA02                       FA06 GA51 JA11

Claims (16)

[Claims] 1. X-rays of a subject are measured at consecutive different tomographic image positions.
A method for detecting a tumor region, which comprises image-processing and measuring a plurality of CT tomographic images obtained by line scanning, and detecting the tumor region based on a three-dimensional shape characteristic parameter of the tumor candidate region. 2. The tumor area detecting method according to claim 1, wherein the three-dimensional shape feature parameter includes at least a sphericity. 3. The tumor area detecting method according to claim 1, wherein the average CT value of the tumor area candidates is also used as a criterion for determining whether the tumor area is a tumor area or not. 4. The tumor region detection method according to claim 1, wherein blood vessel candidates in a plurality of CT tomographic images obtained by X-ray scanning the subject at consecutive different tomographic image positions. A region is extracted based on the CT value, a region having a large volume of a circumscribed rectangular parallelepiped in the blood vessel candidate region is distinguished from a noise region as a blood vessel region, and the blood vessel region is subjected to contraction processing to isolate an isolated region as a tumor. A method for detecting a tumor region, which comprises using a candidate region. 5. The tumor area detection method according to claim 4, wherein a threshold level for tumor detection is provided for each of two or more areas, and contraction processing is performed separately. 6. The method for detecting a tumor region according to claim 4 or 5, wherein a plurality of axial CT images are used,
A method for detecting a tumor region, which comprises detecting a lung field portion based on a CT value, and then extracting a blood vessel candidate region within the lung field portion based on the CT value. 7. The tumor region detecting method according to claim 6, wherein a blood vessel candidate region in a region in which a blood vessel portion in the lung field portion is filled and shaped is extracted based on the CT value. Tumor area detection method. 8. The method for detecting a tumor region according to claim 6 or 7, wherein the blood vessel candidate region in the region formed by three-dimensional image processing of the lung field portion is extracted based on the CT value. A characteristic tumor area detection method. 9. An X-ray image of a subject is taken at consecutive different tomographic image positions.
An image capturing unit that linearly scans to obtain a plurality of CT tomographic images, and a plurality of CT candidate regions for image processing and measurement of the CT tomographic images.
An X-ray CT apparatus, comprising: a tumor area detecting unit that detects a tumor area based on a three-dimensional shape characteristic parameter. 10. The X-ray CT apparatus according to claim 9, wherein the three-dimensional shape feature parameter includes at least a sphericity. 11. X according to claim 9 or 10.
An X-ray CT apparatus characterized in that, in the X-ray CT apparatus, an average CT value of tumor area candidates is also used as a criterion for determining whether or not it is a tumor area. 12. The X-ray CT apparatus according to claim 9, wherein blood vessel candidates in a plurality of CT tomographic images acquired by X-ray scanning the subject at different continuous tomographic image positions. A blood vessel candidate region extracting means for extracting a region based on a CT value;
An X-ray CT apparatus comprising: a tumor candidate region extracting unit that performs a contraction process on the blood vessel region and uses an isolated region as a tumor candidate region. 13. The X-ray CT apparatus according to claim 12, wherein a threshold level for tumor detection is provided for each of two or more regions, and contraction processing is performed separately.
X-ray CT equipment. 14. The X-ray CT apparatus according to claim 12 or 13, wherein a lung field portion is detected from a plurality of axial CT images based on a CT value, and then a blood vessel is detected in the lung field portion. An X-ray CT apparatus characterized by extracting a candidate region based on a CT value. 15. The X-ray CT apparatus according to claim 14, wherein a blood vessel candidate region in a region in which a blood vessel portion in a lung field portion is filled and shaped is extracted based on a CT value. X-ray CT system. 16. The X-ray CT apparatus according to claim 14 or 15, wherein extraction of a blood vessel candidate region in a region formed by performing three-dimensional image processing on a lung field portion is performed based on a CT value. Characteristic X-ray CT device.