KR101927298B1 - Vessel Segmentation in Angiogram - Google Patents
Vessel Segmentation in Angiogram Download PDFInfo
- Publication number
- KR101927298B1 KR101927298B1 KR1020150088563A KR20150088563A KR101927298B1 KR 101927298 B1 KR101927298 B1 KR 101927298B1 KR 1020150088563 A KR1020150088563 A KR 1020150088563A KR 20150088563 A KR20150088563 A KR 20150088563A KR 101927298 B1 KR101927298 B1 KR 101927298B1
- Authority
- KR
- South Korea
- Prior art keywords
- blood vessel
- feature vector
- angiogram
- hessian matrix
- blood vessels
- Prior art date
Links
- 230000011218 segmentation Effects 0.000 title claims description 12
- 210000004204 blood vessel Anatomy 0.000 claims abstract description 62
- 238000000034 method Methods 0.000 claims abstract description 51
- 239000013598 vector Substances 0.000 claims abstract description 43
- 239000011159 matrix material Substances 0.000 claims abstract description 30
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000007637 random forest analysis Methods 0.000 claims description 21
- 238000004458 analytical method Methods 0.000 claims description 12
- 210000001367 artery Anatomy 0.000 claims 1
- 230000007261 regionalization Effects 0.000 claims 1
- 238000010801 machine learning Methods 0.000 abstract description 2
- 230000002708 enhancing effect Effects 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 9
- 238000013146 percutaneous coronary intervention Methods 0.000 description 4
- 230000002792 vascular Effects 0.000 description 4
- 238000013316 zoning Methods 0.000 description 4
- 208000031481 Pathologic Constriction Diseases 0.000 description 3
- 238000003780 insertion Methods 0.000 description 3
- 230000037431 insertion Effects 0.000 description 3
- 230000035945 sensitivity Effects 0.000 description 3
- 208000037804 stenosis Diseases 0.000 description 3
- 230000036262 stenosis Effects 0.000 description 3
- 238000002583 angiography Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000000988 bone and bone Anatomy 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 210000004351 coronary vessel Anatomy 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000005316 response function Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Heart & Thoracic Surgery (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- High Energy & Nuclear Physics (AREA)
- Optics & Photonics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Vascular Medicine (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Psychiatry (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Evolutionary Computation (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Image Analysis (AREA)
Abstract
The present invention relates to a technique of automatically segmenting blood vessels in an angiogram, and is based on a Hessian matrix. In particular, the present invention relates to a method of enhancing a blood vessel, An extraction step of extracting a feature vector by using a high value of the Hessian matrix so as to be able to perform a feature extraction operation on the feature vector, a learning step of machine learning the classifier in a database (CB) To a technique for automatically segmenting blood vessels in an angiogram that includes a test step for testing the blood vessel.
Description
The present invention relates to a technique for automatically segmenting blood vessels in an X-ray angiogram so that a catheter can be inserted in the correct direction.
Percutaneous coronary intervention (PCI) is a two-dimensional x-ray angiography (angiogram) procedure in which an operator inserts a catheter through the skin and places it in the coronary artery, ) And registration of the angiocentric images in three-dimensional CT can serve as a guide for guiding the catheter to be inserted in the correct direction, and it is possible to more precisely measure the degree of stenosis of the narrowed blood vessel .
However, during insertion of the catheter, since the contrasted blood vessel can not be confirmed in real time, it is entirely dependent on the feeling of the operator, and since the image obtained in the procedure is a projection image on the two-dimensional plane, Therefore, it is difficult to ensure the accuracy of the procedure, and there is a problem that the insertion mechanism of the body, such as a stent, may be erroneously inserted and removed.
A typical technique for the vascular segmentation in an X-ray angiogram is the technique of Frangi et al. And the technique of Krissian et al. In both of these techniques, In order to reflect the regional characteristics, we use the high value and eigenvector of Hessian matrix in multi scale.
However, Frangi et al. The technique defines the probability that a particular pixel is a vein using the ratio between the two norms of the two values and the two values, and Krissian et al. We use a response function that utilizes gradient information and gradient information in high resolution and eigenvectors.
Such Frangi et al. Technique and Krissian et al. Technique has a problem in that a false value is significantly increased due to the enhancement of the background noise rather than the blood vessel due to the simple use of simple numerical values such as the ratio and the size.
SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a catheter for guiding a catheter to be inserted in a proper direction, more accurately measure the degree of stenosis of a blood vessel narrowed, It is an object of the present invention to provide a technique for automatically segmenting blood vessels in an angiogram that increases probability.
In order to achieve the above object, the technique of automatically segmenting blood vessels in an angiogram of the present invention is based on a Hessian matrix, and a Hessian matrix of blood vessels An analysis step of analyzing a two-dimensional image into the Hessian matrix to obtain a histogram, and an extraction step of extracting a feature vector using a Hessian matrix of the blood vessel obtained through the analysis step A learning step of generating a database based on the feature vector, a learning step of machine learning a random forest classifier into the database DB created in the generation step, A discriminating step for discriminating between large and small, a determining step for determining whether or not the blood vessel is not to be detected, and a zoning step for zoning the blood vessel.
In the analyzing step, the probability of each pixel being a blood vessel is determined by using a ratio between a vector size of a Hessian matrix of the blood vessel and a high value.
Extraction of the feature vector is used to vector the size of the scale σ n eigenvalues and eigenvectors (λ 1, λ 2) and the eigenvalues and eigenvectors ratio, the eigenvalues and eigenvectors (λ 1, λ 2) of the (λ 1, λ 2) of the And a feature vector is defined. A feature vector of the Hessian matrix is calculated by the number n of scales to define the entire feature vector.
And a testing step of testing the classifier after completing the learning step.
In the test step, the first feature vector of the first pixel of the input image is extracted and input to each of the individual non-leaf nodes of the random forest classifier. Then, all of the leaf nodes ) Of the blood vessel is determined based on the average value of the blood vessels.
And a computer-readable recording medium storing a program for causing a computer to perform a technique of automatically segmenting blood vessels in the angiogram.
According to the present invention, since the Hessian matrix is used, the blood vessel can be efficiently grasped by reflecting the narrow and long-range characteristic of the blood vessel.
By using the random forest classifier, it is possible to increase the probability of detecting a blood vessel, accurately measure the degree of narrowing of the blood vessel, and can effectively perform the procedure, reduce the positive error and increase the probability of the blood vessel, , The procedure time can be reduced, and the burden on both the doctor and the patient can be reduced.
In addition, since the catheter can be guided to be inserted in the correct direction, the safety of the procedure can be ensured, the risk can be reduced, and the degree of narrowing of the narrowed blood vessel can be accurately measured.
1 is a flow chart of a preferred embodiment of the present invention;
Figure 2 is a random forest algorithm diagram of Figure 1;
Fig. 3 is a comparison of the zoning result of Fig. 1 with other techniques. Fig.
4 shows a conventional vascular segmentation technique.
Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of a preferred embodiment of the present invention, FIG. 2 is a view of a random forest algorithm of FIG. 1, and FIG. 3 is a comparison of a zoning result of FIG. 1 and another technique.
Referring to FIG. 1, a technique of automatically segmenting blood vessels in an angiogram according to a preferred embodiment of the present invention includes a method of X-ray (X-ray) (S1) for analyzing an image in the Hessian matrix, and extracting a feature vector using the Hessian matrix of the blood vessel obtained through the analysis step (S1) (S5) of creating a database (DB) built on the basis of the feature vector; and a learning step (S5) of learning a random forest classifier in the database (DB) A determination step S7 for determining whether the feature vector of the database DB is large or small, a determination step S9 for determining whether the feature vector is a blood vessel or not, and a segmentation step S11 for segmenting the blood vessel .
The technique of automatically segmenting blood vessels in the angiogram of the present invention is based on a Hessian matrix. The Hessian matrix is suitable for optimizing reflecting the narrow and long-range characteristics of the blood vessel.
X-rays are taken of a part of the body.
An X-ray image is analyzed with the Hessian matrix to obtain a Hessian matrix of the blood vessel (S1, analysis step).
During the above-described analysis step (S1), when X-ray is taken, various organs including blood vessels, such as bones and diaphragm, are present, in order to view only blood vessels.
That is, it filters the blood vessel based on a Hessian matrix matrix. The Hessian matrix for each pixel in an X-ray image can be said to determine the likelihood that each pixel is a blood vessel through analysis of high values (λ 1 , λ 2 ). The likelihood of each pixel is determined by the vector magnitudes of the first and second magnitudes λ 1 and λ 2 and the vector magnitudes of the first magnitude λ 1 and the second magnitude λ 2 , To be determined.
Based on the high-level analysis, the following equation (1) is calculated to calculate the vesselness value, which means likelihood that the pixel p is a blood vessel.
[Equation 1]
In this case, β and γ mean the thresholds that affect the sensitivity of R β 2 and S, respectively, and V Frangi means Frangi et al. And R β 2 means the ratio of the first high value (λ 1 ) to the second high value (λ 2 ). S is a second order structureness value that increases or decreases according to the structure, and calculates a vector size of the Hessian matrix according to a vector norm calculation method in the Frobenius matrix .
In order to detect blood vessels of various thicknesses (thicknesses), the vesselness value v at a specific scale? In the above equation (1) can be expanded to multiscale, as shown in equation (2).
&Quot; (2) "
The largest vesselness value per pixel
In this case, v (p) means vesselness value at the particular pixel p and, σ means the scale value and, σ min means the minimum value of the scale, and, σ max means the maximum value of the scale, max v (p, σ) means the largest vesselness value and corresponding scale.
A feature vector is extracted using a Hessian matrix of the blood vessel obtained through the analysis step (S1) (S3, extraction step)
In the extraction step (S3), feature vectors are extracted so as to detect blood vessels having various thicknesses.
The feature vector is extracted by combining the high-frequency values at various scales.
The feature vector, the first scale example example the eigenvalues and eigenvectors analysis in (σ 1), the first eigenvalues and eigenvectors (λ 1), the second eigenvalues and eigenvectors (λ 2) and the first eigenvalues and eigenvectors (λ 1) and ( 3 ) using the ratio of the second high value (? 2 ) and the vector magnitude of the first high value (? 1 ) and the second high value (? 2 ).
&Quot; (3) "
In this case, f σ1 (p) means the feature vector of pixel p when the scale is σ 1 (f is an abbreviation for feature), λ 1 means the first high value, and λ 2 means the second high value Max σ v (p, σ) means the largest vesselness value and the corresponding scale, and min σ v (p, σ) means the smallest vesselness value and corresponding scale.
The overall feature vector f (p) is calculated as follows using the Hessian matrix of various scales.
&Quot; (4) "
In this case, n denotes the number of scales to be used, and f (p) denotes a feature vector of pixel p when the scale is multi-scale.
That is, the total feature vector is defined by calculating a high value (? N ) of the Hessian matrix by the number of the scales (? N ).
(DB) based on the entire feature vector (S5, generation step)
The classifier is learned based on the feature vector constructed in the database DB (S5)
The random forest classifier is machine-learned in the database (DB) created in the generation step (S5) (S7, learning step)
The learning step S7 may be performed only at the initial stage of applying the random forest algorithm, storing the learning results, and thereafter using the vascular segmentation technique without going through the learning step S7 based on previously stored learning results Do.
After learning the random forest classifier, the learned classifier is tested.
In the test step, the feature vector f (p) of the pixel p of the input image is extracted and input to the non-leaf nodes of the already learned random forest classifier, and then all the leaf nodes the average value of the leaf nodes has a probability that the corresponding pixel belongs to the blood vessel.
The feature vector of the database DB is determined to be large or small (S9, discrimination step)
In the individual tree learning constituting the random forest classifier, training data randomly extracted from the database (DB) constructed beforehand is used. The decision at each node in the random forest classifier is defined as a thresholding of one of the elements of the feature vector f (p).
As described in (5), in the present invention, a threshold value is calculated from the i-th element of the entire feature vector f (p)
That is, after inputting the feature vector f (p) to each of the non-leaf nodes, an average value of the result values of the generated non-leaf nodes is obtained. This average value determines the probability of the blood vessel. (S11, decision step)
&Quot; (5) "
In this case, θ denotes a threshold value, and θ i j denotes a threshold value of the distance from the i-th element to the j-th element.
As shown in FIG. 2, the random forest is automatically applied to the vascular segmentation technique in the angiogram of the present invention.
When it is determined whether or not the blood vessel is determined (S11), the determined blood vessel is searched (S13)
In order to evaluate the performance of the automatic vessel segmentation algorithm using the random forest classifier in the present invention, the random forest algorithm and the Frangi et al. Technique and Krissian dt al. I compared the results of the automatic segmentation technique.
The results obtained from each technique are compared with the manual segmentation sensitivity and DSC (Dice Similarity Coefficient) as shown in [Table 1]. Compared to the existing two methods, the technique using the random forest algorithm Table 1 shows that a significant decrease in false positives is observed.
In addition, as shown in FIG. 3, when comparing the conventional technique with the automatic technique of the arterial segmentation using the random forest algorithm, the false positive ) Is significantly decreased.
The technique of automatically segmenting the blood vessels in a two-dimensional X-ray angiogram using the random forest algorithm allows the practitioner to more accurately grasp the degree of vessel stenosis, And it is advantageous that the accuracy of the procedure can be improved by inducing the insertion of the catheter in the correct direction through the matching.
A computer-readable recording medium storing a program for causing a computer to execute the above-described method, and a detailed description of a preferred embodiment of the present invention will be given below with reference to the accompanying drawings. It should be noted that the same configurations in the drawings denote the same reference numerals whenever possible. Specific details are set forth in the following description, which is provided to provide a more thorough understanding of the present invention. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.
Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.
May also be a hardware component, such as a processor or circuitry, and / or a software component, executed by a hardware component, such as a processor.
Meanwhile, the above-described embodiments of the present invention can be embodied in a general-purpose digital computer that can be created as a program that can be executed by a computer and operates the program using a computer-readable recording medium.
Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. The computer-readable medium may also include computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
It will be understood by those skilled in the art that the foregoing description of the present invention has been presented for illustrative purposes and that those skilled in the art will readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.
It is intended that the present invention covers the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents. .
As described above, the technique of automatically segmenting the blood vessels in the angiogram according to the present invention is particularly effective in the case of percutaneous coronary intervention (PCI), such as a two-dimensional X-ray angiogram angiogram) is suitable for the technique of automatically segmenting blood vessels.
Claims (6)
An analysis step of analyzing a two-dimensional image into the Hessian matrix to obtain a Hessian matrix of blood vessels;
An extraction step of extracting a feature vector using a Hessian matrix of the blood vessel obtained through the analysis step;
Generating a database (DB) based on the feature vector;
A learning step of mechanically learning a random forest classifier in the database (DB) created in the generating step;
A discriminating step of discriminating the feature vector of the database (DB) from a large one;
A determination step of determining whether or not the blood vessel is a blood vessel;
A method for automatically segmenting blood vessels in an angiogram comprising a segmentation step of segmenting blood vessels.
In the analysis step, an angiogram automatically determines the probability that each pixel is a blood vessel, using a ratio between a vector size of the Hessian matrix of the blood vessel and a high value. Technique for regionalization of blood vessels.
Using the vector magnitude of the eigenvalues and eigenvectors (λ 1, λ 2) and, the eigenvalues and eigenvectors ratio, the eigenvalues and eigenvectors (λ 1, λ 2) of the (λ 1, λ 2) in the feature vector, the scale σ n , ≪ / RTI >
Wherein a total feature vector is defined by calculating a height value of the Hessian matrix by a number n of the scales. The method of automatically segmenting an artery in an angiogram.
A technique for automatically segmenting blood vessels in an angiogram, further comprising a test step after the learning step and testing the classifier.
In the test step, the first feature vector of the first pixel of the input image is extracted and input to each of the individual non-leaf nodes of the random forest classifier. Then, all of the leaf nodes ) Of the blood vessel is determined based on the average value of the angiograms.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020150088563A KR101927298B1 (en) | 2015-06-22 | 2015-06-22 | Vessel Segmentation in Angiogram |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020150088563A KR101927298B1 (en) | 2015-06-22 | 2015-06-22 | Vessel Segmentation in Angiogram |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| KR20170000041A KR20170000041A (en) | 2017-01-02 |
| KR101927298B1 true KR101927298B1 (en) | 2019-03-08 |
Family
ID=57810425
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020150088563A KR101927298B1 (en) | 2015-06-22 | 2015-06-22 | Vessel Segmentation in Angiogram |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR101927298B1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102000615B1 (en) * | 2017-02-22 | 2019-07-16 | 연세대학교 산학협력단 | A method for automatically extracting a starting point of coronary arteries, and an apparatus thereof |
| CN109102511B (en) * | 2018-07-06 | 2021-05-25 | 深圳先进技术研究院 | Cerebrovascular segmentation method, system and electronic equipment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101328729B1 (en) | 2012-09-14 | 2013-11-11 | 이화여자대학교 산학협력단 | Method and apparatus for segmentating vessel in images |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101121396B1 (en) * | 2009-07-31 | 2012-03-05 | 한국과학기술원 | System and method for providing 2-dimensional ct image corresponding to 2-dimensional ultrasound image |
| KR101492330B1 (en) * | 2013-07-26 | 2015-02-11 | 서울여자대학교 산학협력단 | Adaptive vessel segmentation system and the method for CTA |
| KR20150026354A (en) * | 2013-09-02 | 2015-03-11 | 삼성전자주식회사 | Method and Appartus for registering medical images |
| KR20150039641A (en) | 2013-10-02 | 2015-04-13 | 삼성전자주식회사 | X-ray imaging apparatus and image acquiring method using the same |
-
2015
- 2015-06-22 KR KR1020150088563A patent/KR101927298B1/en active IP Right Grant
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101328729B1 (en) | 2012-09-14 | 2013-11-11 | 이화여자대학교 산학협력단 | Method and apparatus for segmentating vessel in images |
Also Published As
| Publication number | Publication date |
|---|---|
| KR20170000041A (en) | 2017-01-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8526699B2 (en) | Method and system for automatic detection and classification of coronary stenoses in cardiac CT volumes | |
| JP6058093B2 (en) | Computer-aided analysis device for medical images and computer program for medical image analysis | |
| US9715637B2 (en) | Method and system for automatic aorta segmentation | |
| US9317918B2 (en) | Apparatus, method, and computer program product for medical diagnostic imaging assistance | |
| EP2365471B1 (en) | Diagnosis assisting apparatus, coronary artery analyzing method and recording medium having a coronary artery analzying program stored therein | |
| Wimmer et al. | A generic probabilistic active shape model for organ segmentation | |
| US10769477B2 (en) | Method, apparatus, device and storage medium for extracting a cardiovisceral vessel from a CTA image | |
| Gupta et al. | Automatic segmentation of cerebrospinal fluid, white and gray matter in unenhanced computed tomography images | |
| US11751823B2 (en) | Image processing apparatus, image processing method, and program | |
| Pock et al. | Multiscale medialness for robust segmentation of 3d tubular structures | |
| US10980496B2 (en) | Heart CT image processing method and apparatus, and non-transitory computer readable storage medium | |
| KR101927298B1 (en) | Vessel Segmentation in Angiogram | |
| Teßmann et al. | Automatic detection and quantification of coronary calcium on 3D CT angiography data | |
| JP4709290B2 (en) | Image processing apparatus and method, and program | |
| EP4209994A1 (en) | Method and system for automatic classification of radiographic images having different acquisition characteristics | |
| TWI490790B (en) | Dynamic cardiac imaging analysis and cardiac function assessment system | |
| Shahzad et al. | Automatic detection of calcified lesions in the descending aorta using contrast enhanced CT scans | |
| Dehkordi | Extraction of the best frames in coronary angiograms for diagnosis and analysis | |
| BE1029246B1 (en) | Computer-implemented method for estimating an input function of a blood vein based on a sequence of three-dimensional volumetric scan images, a data processing device, a computer program product, and a computer-readable storage medium therefor | |
| Beck et al. | Validation and detection of vessel landmarks by using anatomical knowledge | |
| Kang | Automatic Left Ventricle Segmentation using Split Energy Function including Orientation Term from CTA | |
| US11922621B2 (en) | Automatic frame selection for 3D model construction | |
| Oghli et al. | Left ventricle volume measurement on short axis mri images using a combined region growing and superellipse fitting method | |
| Chan et al. | Automatic detection of arterial voxels in dynamic contrast-enhanced MR images of the brain | |
| US11816840B2 (en) | Method, apparatus, device and storage medium for extracting a cardiovascular vessel from a CTA image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A201 | Request for examination | ||
| E902 | Notification of reason for refusal | ||
| E601 | Decision to refuse application | ||
| J201 | Request for trial against refusal decision | ||
| J301 | Trial decision |
Free format text: TRIAL NUMBER: 2017101000675; TRIAL DECISION FOR APPEAL AGAINST DECISION TO DECLINE REFUSAL REQUESTED 20170213 Effective date: 20180912 |
|
| S901 | Examination by remand of revocation | ||
| GRNO | Decision to grant (after opposition) | ||
| GRNT | Written decision to grant |