CN103955610A - Medical image computer-aided analysis method - Google Patents
Medical image computer-aided analysis method Download PDFInfo
- Publication number
- CN103955610A CN103955610A CN201410172988.XA CN201410172988A CN103955610A CN 103955610 A CN103955610 A CN 103955610A CN 201410172988 A CN201410172988 A CN 201410172988A CN 103955610 A CN103955610 A CN 103955610A
- Authority
- CN
- China
- Prior art keywords
- image
- data
- medical image
- voxel
- nodearray
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 claims abstract description 31
- 230000001575 pathological effect Effects 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 14
- 230000007170 pathology Effects 0.000 claims description 26
- 230000003902 lesion Effects 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 7
- 238000001931 thermography Methods 0.000 claims description 7
- 230000006698 induction Effects 0.000 claims description 6
- 238000000151 deposition Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 230000002123 temporal effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004195 computer-aided diagnosis Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Image Processing (AREA)
Abstract
The invention provides a medical image computer-aided analysis method. The medical image computer-aided analysis method comprises the following steps: carrying out primary scanning on a medical image through an infrared light source and an infrared imager, and carrying out image division operation on volume data of the image through the primary scanning so as to obtain primary image data; carrying out secondary scanning on the medical image through a visible light source and a photosensitive element; carrying out image division operation on volume data of the image through the secondary scanning so as to obtain secondary image data; binding the primary image data with the secondary image data, and contrasting with an existing pathological sample in a pathological database, wherein the contrast process specifically comprises mutually contrasting the primary image data and mutually contrasting the secondary image data.
Description
Technical field
The present invention relates to medical instruments field, particularly a kind of medical image computer-aided analysis method.
Background technology
Computer aided detection is to utilize advanced computer software and hardware analysis and process digital radiation image, to find and to detect characteristics of lesion, its result supplies diagnostician's reference as " second suggestion ", help radiation technician to improve Lesion Detection rate, be called as radiologist " the second eyes ", the repeatability that it can improve diagnostic accuracy improvement diagnosis, shortens the read tablet time, increases work efficiency.
Conventional computer aided detection method is method for feature analysis and temporal subtraction.
1, method for feature analysis: extract certain variation characteristic by post processing of image, with the method for exporting again diagnostic result after corresponding pattern match.Method for feature analysis is divided into chooses the step such as region of interest, extraction feature, pattern match and medical diagnosis on disease.Choose after region of interest, apply multiple post processing of image means to selected extracting section feature, carry out pattern match and medical diagnosis on disease according to characteristics of lesion to relevant disease performance.
2, temporal subtraction: patient's two width images are before and after onset reduced to computing mutually, remove the identical part in two width images, retain different piece, obviously outstanding abnormal change under homogeneous background.
Method for feature analysis is to set up in advance pathological model, extracts the pathology region of target image, and characteristics of lesion is mated with pathological model.Because pathological model is fixed model, or the model bank being formed by multiple fixed models, and characteristics of lesion varies, and diagnoses merely according to matching degree, and the situation of erroneous judgement can occur unavoidably.
And temporal subtraction is to reduce mutually computing for patient's two width images before and after onset, instead of single image is analysed and compared, if patient only has single image, the method lost efficacy.
Above-mentioned two kinds of methods, all need to carry out image processing and data processing, specifically comprise following three steps:
(1) intensive treatment of focus shade.At present, mainly adopt spatial filtering and spatial frequency to filter focus contrast is increased, the part contrast beyond focus reduces, so that focus easily detects.
(2) extraction of focus candidate shade.According to threshold values, processing detects focus shade and measures the characteristic quantity of focus, determines whether selected focus.First determine the characteristic quantity yardstick of shade, as the effective diameter about its characteristic of switch amount, circularity, degree of irregularity etc., using the shade large circularity, degree of irregularity is little as the scale that extracts inactivity of yang-qi shadow, then through repeatedly threshold values processing, obtain black and white contrast image.
(3) dwindle focus candidate shade scope (reduction false positive).Utilize image recognition technology to select to reflect the characteristic quantity of focus shade, according to characteristic quantity identification focus and false positive shade, characteristic quantity is more, occurs that false positive number is just fewer.As used more than 10 characteristic quantities such as effective diameter, circularity, density in tubercle shadow, then characteristic quantity is carried out to diagnostic classification, common method artificial neural network method, fuzzy clustering algorithm, linear classification etc.
In above-mentioned steps, the intensive treatment step of focus shade is most important, is the basis of two diagnostic process steps next, if deviation appears in this step, deviation can cause chain deviation in follow-up two steps, and this deviation is even exaggerated, and causes mistaken diagnosis.
Therefore, how setting up a complete pathology storehouse, store pathological model as much as possible, improve the intensifying treatment effect of focus shade, is current problem demanding prompt solution.
Summary of the invention
The present invention proposes a kind of medical image computer-aided analysis method, by setting up and constantly update pathological data storehouse, will medical image be diagnosed and existing pathology sample compare, the correctness that improves auxiliary diagnosis, has solved the problem that existing computer aided diagnosing method pathological model is single, focus shade exists error.
Technical scheme of the present invention is achieved in that
A kind of medical image computer-aided analysis method, comprises the following steps:
Step (1), carries out single pass by infrared light supply and infrared thermography to medical image, and the volume data of single pass image is carried out to image splitting operation, obtains view data one time;
Described image splitting operation specifically comprises the following steps:
Step (a), create a division region child node set setNodes, with a data area NodeArray with volume data formed objects, the address of described data area NodeArray for depositing each voxel place child node, and the data content of data area NodeArray is set to sky;
Step (b), travels through each the tissue points Voxel (xi, yi, zi) in volume data successively, when completing after traversal, jumps to step (g);
Step (c), judges whether Voxel (xi, yi, zi) was expanded; Determination methods is for seeing whether the value in its corresponding NodeArray (xi, yi, zi) is empty; If expanded, return to step (b);
Step (d), creates a sub regions node Nodej, and the reference position of this node is Voxel (xi, yi, zi);
Step (e), expands the positive dirction of X, Y, tri-coordinate axis of Z respectively, and whether voxel and the Voxel (xi, yi, zi) of the new expansion of judgement have feature consistency; In the time that inconsistent voxel appears in a direction, stop the expansion of this direction;
Step (f), can obtain the end position Voxel (xi+m, yi+n, zi+k) of node Nodej after described step (e) completes; The value of voxel Voxel (xs, ys, zs) the corresponding NodeArray (xs, ys, zs) first Nodej being comprised is set to the address of Nodej, then is added in child node set setNodes;
Step (g), having traveled through in volume data after all voxels, by the data in the NodeArray of ergodic data region, generates the neighbouring relations of subregion node;
Step (2), carries out rescan by visible light source and photo-sensitive cell to described medical image; The volume data of rescan image is carried out to described image splitting operation, obtain secondary image data;
Step (3), by a described view data and secondary image data binding, compares with existing pathology sample in pathological data storehouse, and comparison process is specially a view data and compares mutually, and secondary image data is compared mutually;
Step (4), one time view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 is multiplied by quadratic coefficients k2, k1+k2=1, and 0.2≤k1≤0.3,0.7≤k1≤0.8; If r1*k1+r2*k2 >=0.5, judges that new pathology sample is pathology sample, and pathological data storehouse described in typing; If 0.05≤r1*k1+r2*k2 < 0.5, judges that new pathology sample is suspected lesion sample; If r1*k1+r2*k2 < 0.05, judges that pathology sample is normal sample.
Alternatively, in described step (1), by the inswept medical image of column infrared light supply, the infrared thermography that is positioned at described medical image opposite side carries out induction image forming to the infrared signal after filtering through medical image film, obtains single pass image.
Alternatively, in described step (2), by the inswept medical image of column visible light source, the photo-sensitive cell that is positioned at described medical image opposite side carries out induction image forming to the visible light signal after filtering through medical image film, obtains rescan image.
Alternatively, in described step (3), in step that in pathological data storehouse, existing pathology sample compares, also comprise the step of existing pathology sample being carried out to decompress(ion), decompress(ion) is view data and secondary image data.
The invention has the beneficial effects as follows:
(1) focus accurate positioning, focus dimensional measurement error is little;
And set up a set of complete pathological data storehouse (2), and constantly data storehouse is upgraded, improved the accuracy of diagnosis.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the process flow diagram of medical image computer aided diagnosing method of the present invention;
Fig. 2 is the process flow diagram of image splitting operation process in medical image computer aided diagnosing method of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Computer-aided analysis has become reference frame important in diagnosis process, but existing computer-aided diagnosis is all in medical image shooting process, to carry out image processing and data processing, after medical image film printing, have with sheet report, doctor makes final judgement according to report and the observation of oneself.If patient is to other hospital admissions, doctor can only diagnose by rule of thumb, or needs of patients carries out filming image again one time, and the health to patient or economy are all a kind of losses.
Medical image computer-aided analysis method of the present invention, by infrared light and visible ray, medical image film is carried out to twice sweep, then scan image is carried out to image splitting operation, obtain twice sweep data respectively with pathology storehouse in pathology sample compare, comparison result is multiplied by weight and obtains last diagnostic result.Below in conjunction with accompanying drawing, method of the present invention is described in detail.
As shown in Figure 1, medical image computer-aided analysis method of the present invention, comprises the following steps:
Step (1), carries out single pass by infrared light supply and infrared thermography to medical image, and the volume data of single pass image is carried out to image splitting operation, obtains view data one time.The infrared light of infrared light supply is filtered while seeing through image film, and infrared light is different in the transmitance of focus and other parts, therefore the imaging difference on infrared thermography.Single pass can be strengthened the location to lesions position, and focus size is carried out to preliminary surveying.
As shown in Figure 2, described image splitting operation specifically comprises the following steps:
Step (a), create a division region child node set setNodes, with a data area NodeArray with volume data formed objects, the address of described NodeArray for depositing each voxel place child node, and the data content of NodeArray is set to sky;
Step (b), travels through each the tissue points Voxel (xi, yi, zi) in volume data successively, when completing after traversal, jumps to step (g);
Step (c), judges whether Voxel (xi, yi, zi) was expanded; Determination methods is for seeing whether the value in its corresponding NodeArray (xi, yi, zi) is empty; If expanded, return to step (b);
Step (d), creates a sub regions node Nodej, and the reference position of this node is Voxel (xi, yi, zi);
Step (e), expands the positive dirction of X, Y, tri-coordinate axis of Z respectively, and whether voxel and the Voxel (xi, yi, zi) of the new expansion of judgement have feature consistency; In the time that inconsistent voxel appears in a direction, stop the expansion of this direction;
Step (f), can obtain the end position Voxel (xi+m, yi+n, zi+k) of node Nodej after described step (e) completes; The value of voxel Voxel (xs, ys, zs) the corresponding NodeArray (xs, ys, zs) first Nodej being comprised is set to the address of Nodej, then is added in child node set setNodes;
Step (g), having traveled through in volume data after all voxels, by the data in traversal NodeArray, generates the neighbouring relations of subregion node.
Image splitting operation process of the present invention only, by twice traversal volume data, just can divide volume data, and region after dividing there will not be the situation of Hypersegmentation.
Get back to process flow diagram shown in Fig. 1, method of the present invention also comprises:
Step (2), carries out rescan by visible light source and photo-sensitive cell to described medical image; The volume data of rescan image is carried out to described image splitting operation, obtain secondary image data.Image splitting operation process is identical with process shown in Fig. 2, repeats no more here.Visible ray is filtered while seeing through image film, and visible ray is good at the sharpen effect at focus edge, can improve focus contrast, improves image flatness, obtains focus size accurately.
Step (3), by a described view data and secondary image data binding, compares with existing pathology sample in pathological data storehouse, and comparison process is specially a view data and compares mutually, and secondary image data is compared mutually.
Step (4), one time view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 is multiplied by quadratic coefficients k2, k1+k2=1, and k1≤0.3, k2 >=0.7; If r1*k1+r2*k2 >=0.5, judges that new pathology sample is pathology sample, and typing pathological data storehouse; If 0.05≤r1*k1+r2*k2 < 0.5, judges that new pathology sample is suspected lesion sample; If r1*k1+r2*k2 < 0.05, judges that pathology sample is normal sample.
Preferably, in described step (1), by the inswept medical image of column infrared light supply, the infrared thermography that is positioned at described medical image opposite side carries out induction image forming to the infrared signal after filtering through medical image film, obtains single pass image.
Preferably, in described step (2), by the inswept medical image of column visible light source, the photo-sensitive cell that is positioned at described medical image opposite side carries out induction image forming to the visible light signal after filtering through medical image film, obtains rescan image.
Preferably, in described step (3), in step that in pathological data storehouse, existing pathology sample compares, also comprise the step of existing pathology sample being carried out to decompress(ion), decompress(ion) is view data and secondary image data.
Medical image computer-aided analysis method of the present invention, focus accurate positioning, focus dimensional measurement error is little; And set up a set of complete pathological data storehouse, and constantly data storehouse is upgraded, the accuracy of diagnosis improved.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (4)
1. a medical image computer-aided analysis method, is characterized in that, comprises the following steps:
Step (1), carries out single pass by infrared light supply and infrared thermography to medical image, and the volume data of single pass image is carried out to image splitting operation, obtains view data one time;
Described image splitting operation specifically comprises the following steps:
Step (a), create a division region child node set setNodes, with a data area NodeArray with volume data formed objects, the address of described data area NodeArray for depositing each voxel place child node, and the data content of data area NodeArray is set to sky;
Step (b), travels through each the tissue points Voxel (xi, yi, zi) in volume data successively, when completing after traversal, jumps to step (g);
Step (c), judges whether Voxel (xi, yi, zi) was expanded; Determination methods is for seeing whether the value in its corresponding NodeArray (xi, yi, zi) is empty; If expanded, return to step (b);
Step (d), creates a sub regions node Nodej, and the reference position of this node is Voxel (xi, yi, zi);
Step (e), expands the positive dirction of X, Y, tri-coordinate axis of Z respectively, and whether voxel and the Voxel (xi, yi, zi) of the new expansion of judgement have feature consistency; In the time that inconsistent voxel appears in a direction, stop the expansion of this direction;
Step (f), can obtain the end position Voxel (xi+m, yi+n, zi+k) of node Nodej after described step (e) completes; The value of voxel Voxel (xs, ys, zs) the corresponding NodeArray (xs, ys, zs) first Nodej being comprised is set to the address of Nodej, then is added in child node set setNodes;
Step (g), having traveled through in volume data after all voxels, by the data in the NodeArray of ergodic data region, generates the neighbouring relations of subregion node;
Step (2), carries out rescan by visible light source and photo-sensitive cell to described medical image; The volume data of rescan image is carried out to described image splitting operation, obtain secondary image data;
Step (3), by a described view data and secondary image data binding, compares with existing pathology sample in pathological data storehouse, and comparison process is specially a view data and compares mutually, and secondary image data is compared mutually;
Step (4), one time view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 is multiplied by quadratic coefficients k2, k1+k2=1, and 0.2≤k1≤0.3,0.7≤k1≤0.8; If r1*k1+r2*k2 >=0.5, judges that new pathology sample is pathology sample, and pathological data storehouse described in typing; If 0.05≤r1*k1+r2*k2 < 0.5, judges that new pathology sample is suspected lesion sample; If r1*k1+r2*k2 < 0.05, judges that pathology sample is normal sample.
2. medical image computer-aided analysis method as claimed in claim 1, it is characterized in that, in described step (1), by the inswept medical image of column infrared light supply, the infrared thermography that is positioned at described medical image opposite side carries out induction image forming to the infrared signal after filtering through medical image film, obtains single pass image.
3. medical image computer-aided analysis method as claimed in claim 1, it is characterized in that, in described step (2), by the inswept medical image of column visible light source, the photo-sensitive cell that is positioned at described medical image opposite side carries out induction image forming to the visible light signal after filtering through medical image film, obtains rescan image.
4. medical image computer-aided analysis method as claimed in claim 1, it is characterized in that, in described step (3), in step that in pathological data storehouse, existing pathology sample compares, also comprise the step of existing pathology sample being carried out to decompress(ion), decompress(ion) is view data and secondary image data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410172988.XA CN103955610B (en) | 2014-04-22 | 2014-04-22 | Medical image computer-aided analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410172988.XA CN103955610B (en) | 2014-04-22 | 2014-04-22 | Medical image computer-aided analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103955610A true CN103955610A (en) | 2014-07-30 |
| CN103955610B CN103955610B (en) | 2017-04-26 |
Family
ID=51332885
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410172988.XA Active CN103955610B (en) | 2014-04-22 | 2014-04-22 | Medical image computer-aided analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103955610B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046111A (en) * | 2015-09-10 | 2015-11-11 | 济南市儿童医院 | Amplitude integrated electroencephalogram result automatic identifying system and method |
| CN108109682A (en) * | 2018-01-19 | 2018-06-01 | 大连外国语大学 | A kind of medical image identifying system and its method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030095692A1 (en) * | 2001-11-20 | 2003-05-22 | General Electric Company | Method and system for lung disease detection |
| US20110071384A1 (en) * | 2006-11-22 | 2011-03-24 | Kabushiki Kaisha Toshiba | Magnetic resonance imaging apparatus |
| CN103282790A (en) * | 2010-12-21 | 2013-09-04 | 皇家飞利浦电子股份有限公司 | Fast dual contrast mr imaging |
| CN103608839A (en) * | 2011-03-28 | 2014-02-26 | 皇家飞利浦有限公司 | Contrast-dependent resolution image |
-
2014
- 2014-04-22 CN CN201410172988.XA patent/CN103955610B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030095692A1 (en) * | 2001-11-20 | 2003-05-22 | General Electric Company | Method and system for lung disease detection |
| US20110071384A1 (en) * | 2006-11-22 | 2011-03-24 | Kabushiki Kaisha Toshiba | Magnetic resonance imaging apparatus |
| CN103282790A (en) * | 2010-12-21 | 2013-09-04 | 皇家飞利浦电子股份有限公司 | Fast dual contrast mr imaging |
| CN103608839A (en) * | 2011-03-28 | 2014-02-26 | 皇家飞利浦有限公司 | Contrast-dependent resolution image |
Non-Patent Citations (1)
| Title |
|---|
| 刘俊 等: "基于图像特征检索和识别的医学影像辅助诊断系统", 《中国科技信息》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046111A (en) * | 2015-09-10 | 2015-11-11 | 济南市儿童医院 | Amplitude integrated electroencephalogram result automatic identifying system and method |
| CN105046111B (en) * | 2015-09-10 | 2018-09-25 | 济南市儿童医院 | A kind of Amplitude integrated electroencephalogram result automatic recognition system |
| CN108109682A (en) * | 2018-01-19 | 2018-06-01 | 大连外国语大学 | A kind of medical image identifying system and its method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103955610B (en) | 2017-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109035187B (en) | Medical image labeling method and device | |
| Chauhan et al. | Role of gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation | |
| CN111080584B (en) | Quality control method for medical image, computer device and readable storage medium | |
| US20220198230A1 (en) | Auxiliary detection method and image recognition method for rib fractures based on deep learning | |
| CN110782489B (en) | Image data matching method, device and equipment and computer readable storage medium | |
| Vos et al. | Computational modeling for assessment of IBD: to be or not to be? | |
| Du et al. | Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN | |
| US9811904B2 (en) | Method and system for determining a phenotype of a neoplasm in a human or animal body | |
| KR101645292B1 (en) | System and method for automatic planning of two-dimensional views in 3d medical images | |
| CN111161848B (en) | Focus marking method and device for CT image and storage medium | |
| CN110838114B (en) | Pulmonary nodule detection method, device and computer storage medium | |
| CN104093354A (en) | Method and apparatus for assessment of medical images | |
| US11471096B2 (en) | Automatic computerized joint segmentation and inflammation quantification in MRI | |
| CN111932492B (en) | Medical image processing method and device and computer readable storage medium | |
| CN113034528A (en) | Target area and organ-at-risk delineation contour accuracy testing method based on image omics | |
| CN113240606B (en) | Traditional Chinese medicine inspection image color correction method and system | |
| US11783476B2 (en) | System and method for analyzing three-dimensional image data | |
| Kim et al. | Image biomarkers for quantitative analysis of idiopathic interstitial pneumonia | |
| CN112529900A (en) | Method, device, terminal and storage medium for matching ROI in mammary gland image | |
| CN103955610A (en) | Medical image computer-aided analysis method | |
| CN116363104A (en) | Automatic diagnosis equipment and system for image medicine | |
| CN112862804B (en) | System and method for processing retina blood vessel image | |
| Skoura et al. | Analyzing tree-shape anatomical structures using topological descriptors of branching and ensemble of classifiers | |
| US20240087751A1 (en) | Systems and methods for organ shape analysis for disease diagnosis and risk assessment | |
| EP4040384A1 (en) | Method, device and a system for determining presence of appendicitis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Han Yan Inventor after: Dong San Inventor after: Chang Xiaofeng Inventor after: Wei Bin Inventor after: Zhang Kunpeng Inventor after: Niu Haitao Inventor after: Zhu Chengzhan Inventor before: Han Yan |
|
| CB03 | Change of inventor or designer information | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20170321 Address after: 266000 Shandong city of Qingdao province Jiangsu City Road No. 16 Applicant after: Affiliated Hospital of University Of Qingdao Address before: 266000 Shandong city of Qingdao province Jiangsu City Road No. 16 Applicant before: Han Yan |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |