CN113781440A - 超声视频病灶检测方法及装置 - Google Patents
超声视频病灶检测方法及装置 Download PDFInfo
- Publication number
- CN113781440A CN113781440A CN202111065766.4A CN202111065766A CN113781440A CN 113781440 A CN113781440 A CN 113781440A CN 202111065766 A CN202111065766 A CN 202111065766A CN 113781440 A CN113781440 A CN 113781440A
- Authority
- CN
- China
- Prior art keywords
- feature matrix
- matrix
- image
- network
- feature
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 54
- 239000011159 matrix material Substances 0.000 claims abstract description 103
- 230000003902 lesion Effects 0.000 claims abstract description 39
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000002604 ultrasonography Methods 0.000 claims description 41
- 238000012545 processing Methods 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 9
- 230000009467 reduction Effects 0.000 claims description 8
- 230000007246 mechanism Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 230000007787 long-term memory Effects 0.000 claims description 4
- 230000006403 short-term memory Effects 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 description 23
- 238000013473 artificial intelligence Methods 0.000 description 18
- 210000000481 breast Anatomy 0.000 description 15
- 238000004364 calculation method Methods 0.000 description 9
- 238000003745 diagnosis Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 9
- 238000013461 design Methods 0.000 description 8
- 238000012549 training Methods 0.000 description 7
- 206010028980 Neoplasm Diseases 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 210000005075 mammary gland Anatomy 0.000 description 6
- 238000013135 deep learning Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000010606 normalization Methods 0.000 description 5
- 238000011897 real-time detection Methods 0.000 description 5
- 206010006187 Breast cancer Diseases 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 4
- 230000003321 amplification Effects 0.000 description 4
- 238000003199 nucleic acid amplification method Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 208000026310 Breast neoplasm Diseases 0.000 description 3
- 230000002308 calcification Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000002372 labelling Methods 0.000 description 3
- 210000001165 lymph node Anatomy 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 230000036210 malignancy Effects 0.000 description 2
- 230000003211 malignant effect Effects 0.000 description 2
- 238000011176 pooling Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 210000004872 soft tissue Anatomy 0.000 description 2
- 101150064138 MAP1 gene Proteins 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000010339 dilation Effects 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000006651 lactation Effects 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 230000035935 pregnancy Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
Description
Claims (12)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111065766.4A CN113781440B (zh) | 2020-11-25 | 2020-11-25 | 超声视频病灶检测方法及装置 |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111065766.4A CN113781440B (zh) | 2020-11-25 | 2020-11-25 | 超声视频病灶检测方法及装置 |
CN202011333447.2A CN112446862B (zh) | 2020-11-25 | 2020-11-25 | 一种基于人工智能的动态乳腺超声视频全病灶实时检测和分割装置、***及图像处理方法 |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011333447.2A Division CN112446862B (zh) | 2020-11-25 | 2020-11-25 | 一种基于人工智能的动态乳腺超声视频全病灶实时检测和分割装置、***及图像处理方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113781440A true CN113781440A (zh) | 2021-12-10 |
CN113781440B CN113781440B (zh) | 2022-07-29 |
Family
ID=74738761
Family Applications (3)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111065625.2A Active CN113781439B (zh) | 2020-11-25 | 2020-11-25 | 超声视频病灶分割方法及装置 |
CN202011333447.2A Active CN112446862B (zh) | 2020-11-25 | 2020-11-25 | 一种基于人工智能的动态乳腺超声视频全病灶实时检测和分割装置、***及图像处理方法 |
CN202111065766.4A Active CN113781440B (zh) | 2020-11-25 | 2020-11-25 | 超声视频病灶检测方法及装置 |
Family Applications Before (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111065625.2A Active CN113781439B (zh) | 2020-11-25 | 2020-11-25 | 超声视频病灶分割方法及装置 |
CN202011333447.2A Active CN112446862B (zh) | 2020-11-25 | 2020-11-25 | 一种基于人工智能的动态乳腺超声视频全病灶实时检测和分割装置、***及图像处理方法 |
Country Status (1)
Country | Link |
---|---|
CN (3) | CN113781439B (zh) |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113239951B (zh) * | 2021-03-26 | 2024-01-30 | 无锡祥生医疗科技股份有限公司 | 超声乳腺病灶的分类方法、装置及存储介质 |
CN113344028A (zh) * | 2021-05-10 | 2021-09-03 | 深圳瀚维智能医疗科技有限公司 | 乳腺超声序列图像分类方法及装置 |
CN113344855A (zh) * | 2021-05-10 | 2021-09-03 | 深圳瀚维智能医疗科技有限公司 | 降低乳腺超声病灶检测假阳率的方法、装置、设备及介质 |
CN113902670B (zh) * | 2021-08-31 | 2022-07-29 | 北京医准智能科技有限公司 | 一种基于弱监督学习的超声视频分割方法及装置 |
CN114091507B (zh) * | 2021-09-02 | 2022-07-29 | 北京医准智能科技有限公司 | 超声病灶区域检测方法、装置、电子设备及存储介质 |
CN113855079A (zh) * | 2021-09-17 | 2021-12-31 | 上海仰和华健人工智能科技有限公司 | 基于乳腺超声影像的实时检测和乳腺疾病辅助分析方法 |
CN114155193B (zh) * | 2021-10-27 | 2022-07-26 | 北京医准智能科技有限公司 | 一种基于特征强化的血管分割方法及装置 |
CN114764812A (zh) * | 2022-03-14 | 2022-07-19 | 什维新智医疗科技(上海)有限公司 | 一种病灶区域分割装置 |
CN114764811B (zh) * | 2022-03-14 | 2024-07-09 | 什维新智医疗科技(上海)有限公司 | 一种基于动态超声视频的病灶区域实时分割装置 |
CN116309585B (zh) * | 2023-05-22 | 2023-08-22 | 山东大学 | 基于多任务学习的乳腺超声图像目标区域识别方法及*** |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080002873A1 (en) * | 2000-04-11 | 2008-01-03 | Cornell Research Foundation, Inc. | System and method for three-dimensional image rendering and analysis |
CN108364006A (zh) * | 2018-01-17 | 2018-08-03 | 超凡影像科技股份有限公司 | 基于多模式深度学习的医学图像分类装置及其构建方法 |
CN110490863A (zh) * | 2019-08-22 | 2019-11-22 | 北京红云智胜科技有限公司 | 基于深度学习的检测冠脉造影有无完全闭塞病变的*** |
CN111210443A (zh) * | 2020-01-03 | 2020-05-29 | 吉林大学 | 基于嵌入平衡的可变形卷积混合任务级联语义分割方法 |
CN111462049A (zh) * | 2020-03-09 | 2020-07-28 | 西南交通大学 | 一种乳腺超声造影视频中病灶区形态自动标注方法 |
CN111784701A (zh) * | 2020-06-10 | 2020-10-16 | 深圳市人民医院 | 结合边界特征增强和多尺度信息的超声图像分割方法及*** |
Family Cites Families (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP6964234B2 (ja) * | 2016-11-09 | 2021-11-10 | パナソニックIpマネジメント株式会社 | 情報処理方法、情報処理装置およびプログラム |
CN106846306A (zh) * | 2017-01-13 | 2017-06-13 | 重庆邮电大学 | 一种超声图像自动描述方法和*** |
US10140710B2 (en) * | 2017-03-09 | 2018-11-27 | Kevin Augustus Kreeger | Automatic key frame detection |
CN107451615A (zh) * | 2017-08-01 | 2017-12-08 | 广东工业大学 | 基于Faster RCNN的甲状腺***状癌超声图像识别方法及*** |
US10223610B1 (en) * | 2017-10-15 | 2019-03-05 | International Business Machines Corporation | System and method for detection and classification of findings in images |
CN108399419B (zh) * | 2018-01-25 | 2021-02-19 | 华南理工大学 | 基于二维递归网络的自然场景图像中中文文本识别方法 |
CN108665456B (zh) * | 2018-05-15 | 2022-01-28 | 广州尚医网信息技术有限公司 | 基于人工智能的乳腺超声病灶区域实时标注的方法及*** |
CN109191442B (zh) * | 2018-08-28 | 2021-04-13 | 深圳大学 | 超声图像评估及筛选方法和装置 |
CN109830303A (zh) * | 2019-02-01 | 2019-05-31 | 上海众恒信息产业股份有限公司 | 基于互联网一体化医疗平台的临床数据挖掘分析与辅助决策方法 |
CN110047068A (zh) * | 2019-04-19 | 2019-07-23 | 山东大学 | 基于金字塔场景分析网络的mri脑肿瘤分割方法及*** |
CN110288597B (zh) * | 2019-07-01 | 2021-04-02 | 哈尔滨工业大学 | 基于注意力机制的无线胶囊内窥镜视频显著性检测方法 |
CN110674845B (zh) * | 2019-08-28 | 2022-05-31 | 电子科技大学 | 一种结合多感受野注意与特征再校准的菜品识别方法 |
CN110674866B (zh) * | 2019-09-23 | 2021-05-07 | 兰州理工大学 | 迁移学习特征金字塔网络对X-ray乳腺病灶图像检测方法 |
CN110705457B (zh) * | 2019-09-29 | 2024-01-19 | 核工业北京地质研究院 | 一种遥感影像建筑物变化检测方法 |
CN111145170B (zh) * | 2019-12-31 | 2022-04-22 | 电子科技大学 | 一种基于深度学习的医学影像分割方法 |
CN111227864B (zh) * | 2020-01-12 | 2023-06-09 | 刘涛 | 使用超声图像利用计算机视觉进行病灶检测的装置 |
CN111539930B (zh) * | 2020-04-21 | 2022-06-21 | 浙江德尚韵兴医疗科技有限公司 | 基于深度学习的动态超声乳腺结节实时分割与识别的方法 |
CN111695592B (zh) * | 2020-04-27 | 2024-07-09 | 平安科技(深圳)有限公司 | 基于可变形卷积的图像识别方法、装置、计算机设备 |
CN111667459B (zh) * | 2020-04-30 | 2023-08-29 | 杭州深睿博联科技有限公司 | 一种基于3d可变卷积和时序特征融合的医学征象检测方法、***、终端及存储介质 |
CN111915573A (zh) * | 2020-07-14 | 2020-11-10 | 武汉楚精灵医疗科技有限公司 | 一种基于时序特征学习的消化内镜下病灶跟踪方法 |
AU2020101581A4 (en) * | 2020-07-31 | 2020-09-17 | Ampavathi, Anusha MS | Lymph node metastases detection from ct images using deep learning |
CN111709950B (zh) * | 2020-08-20 | 2020-11-06 | 成都金盘电子科大多媒体技术有限公司 | 一种乳腺钼靶ai辅助筛查方法 |
CN112132833B (zh) * | 2020-08-25 | 2024-03-26 | 沈阳工业大学 | 一种基于深度卷积神经网络的皮肤病图像病灶分割方法 |
CN112489060B (zh) * | 2020-12-07 | 2022-05-10 | 北京医准智能科技有限公司 | 一种用于肺炎病灶分割的***及方法 |
-
2020
- 2020-11-25 CN CN202111065625.2A patent/CN113781439B/zh active Active
- 2020-11-25 CN CN202011333447.2A patent/CN112446862B/zh active Active
- 2020-11-25 CN CN202111065766.4A patent/CN113781440B/zh active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080002873A1 (en) * | 2000-04-11 | 2008-01-03 | Cornell Research Foundation, Inc. | System and method for three-dimensional image rendering and analysis |
CN108364006A (zh) * | 2018-01-17 | 2018-08-03 | 超凡影像科技股份有限公司 | 基于多模式深度学习的医学图像分类装置及其构建方法 |
CN110490863A (zh) * | 2019-08-22 | 2019-11-22 | 北京红云智胜科技有限公司 | 基于深度学习的检测冠脉造影有无完全闭塞病变的*** |
CN111210443A (zh) * | 2020-01-03 | 2020-05-29 | 吉林大学 | 基于嵌入平衡的可变形卷积混合任务级联语义分割方法 |
CN111462049A (zh) * | 2020-03-09 | 2020-07-28 | 西南交通大学 | 一种乳腺超声造影视频中病灶区形态自动标注方法 |
CN111784701A (zh) * | 2020-06-10 | 2020-10-16 | 深圳市人民医院 | 结合边界特征增强和多尺度信息的超声图像分割方法及*** |
Non-Patent Citations (2)
Title |
---|
SIYUAN QIAO ET.AL: "DetectoRS: detecting objects with recursive feature pyramid and switchable atrous convolution", 《ARXIV:2006.02334》 * |
李梦奇: "基于卷积特征的可变形部件模型的人体检测和行为识别研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Also Published As
Publication number | Publication date |
---|---|
CN112446862A (zh) | 2021-03-05 |
CN113781439B (zh) | 2022-07-29 |
CN113781440B (zh) | 2022-07-29 |
CN112446862B (zh) | 2021-08-10 |
CN113781439A (zh) | 2021-12-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113781440B (zh) | 超声视频病灶检测方法及装置 | |
US11101033B2 (en) | Medical image aided diagnosis method and system combining image recognition and report editing | |
Su et al. | Lung nodule detection based on faster R-CNN framework | |
US10614573B2 (en) | Method for automatically recognizing liver tumor types in ultrasound images | |
Li et al. | Dilated-inception net: multi-scale feature aggregation for cardiac right ventricle segmentation | |
CN111227864B (zh) | 使用超声图像利用计算机视觉进行病灶检测的装置 | |
CN108464840B (zh) | 一种乳腺肿块自动检测方法及*** | |
CN110490851B (zh) | 基于人工智能的乳腺图像分割方法、装置及*** | |
CN112086197B (zh) | 基于超声医学的乳腺结节检测方法及*** | |
CN111429474B (zh) | 基于混合卷积的乳腺dce-mri图像病灶分割模型建立及分割方法 | |
CN109858540B (zh) | 一种基于多模态融合的医学图像识别***及方法 | |
CN111214255B (zh) | 一种医学超声图像计算机辅助方法 | |
CN110490892A (zh) | 一种基于USFaster R-CNN的甲状腺超声图像结节自动定位识别方法 | |
CN110858399B (zh) | 用于提供虚拟断层扫描中风后续检查图像的方法和装置 | |
CN114782307A (zh) | 基于深度学习的增强ct影像直肠癌分期辅助诊断*** | |
CN111583385B (zh) | 一种可变形数字人解剖学模型的个性化变形方法及*** | |
CN111429457B (zh) | 图像局部区域亮度智能评价方法、装置、设备及介质 | |
CN110648333B (zh) | 基于中智学理论的乳腺超声视频图像实时分割*** | |
CN116665896A (zh) | 预测乳腺癌腋窝***转移的模型建立方法 | |
Huang et al. | Thyroid Nodule Classification in Ultrasound Videos by Combining 3D CNN and Video Transformer | |
Asha et al. | Segmentation of Brain Tumors using traditional Multiscale bilateral Convolutional Neural Networks | |
Mohamed et al. | Advancing Cardiac Image Processing: An Innovative Model Utilizing Canny Edge Detection For Enhanced Diagnostics | |
Simangunsong et al. | Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method | |
Fei et al. | Medical Image enhancement based on frame accumulation and registration technology | |
Yasrab et al. | Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CP03 | Change of name, title or address |
Address after: Room 3011, 2nd Floor, Building A, No. 1092 Jiangnan Road, Nanmingshan Street, Liandu District, Lishui City, Zhejiang Province, 323000 Patentee after: Zhejiang Yizhun Intelligent Technology Co.,Ltd. Patentee after: Guangxi Yizhun Intelligent Technology Co.,Ltd. Address before: No. 1202-1203, 12 / F, block a, Zhizhen building, No. 7, Zhichun Road, Haidian District, Beijing 100083 Patentee before: Beijing Yizhun Intelligent Technology Co.,Ltd. Patentee before: Guangxi Yizhun Intelligent Technology Co.,Ltd. |
|
CP03 | Change of name, title or address |