An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking

doi:10.1166/jmihi.2018.2288

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 自动推理与认知研究中心

	An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking
	Wang, Weihua 1,2,3; Zhang, Jingzhong 1; Wu, Wenyuan1 ; Zhou, Shuang 4
刊名	JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
	2018-02-01
卷号	8 期号:2 页码:262-274
关键词	Vessel Contrast Medical Image Processing Automatic Segmentation Mathematical Morphology Vessel Segmentation
ISSN号	2156-7018
DOI	10.1166/jmihi.2018.2288
英文摘要	We proposed a complete algorithm for enhancing and segmenting retinal vessel using multi-scale morphology and seed point tracking approach. First, the high contrast blood vessel images at each scale are obtained by the comprehensive application of top-hat and bottom-hat transformation enhancement technology with line structuring elements, the bright and dark areas, whose diameters are greater than the scale of the structuring element, can be filtered in this stage. Second, the blood vessel image is segmented by multi-threshold based vessel tracking technology. In the tracking stage, the thresholds are adaptively obtained using the proportion of the blood vessel pixels, and the stop condition can be automatically calculated in this process. The performance of our proposed method is assessed on the publicly available DRIVE and STARE fundus image datasets. For database DRIVE, the proposed method has achieved accuracy, specificity and sensitivity of 0.9449, 0.9810 and 0.7236 respectively, and 0.9460, 0.9680 and 0.7486 for database STARE respectively. The segmentation result of our novel algorithm is better than the other unsupervised methods. Our new technique is robust to the pathological cases, it improves the segmentation accuracy and decreases the false segmentation near the large bright and dark areas, such as optic disc, hard exudate, fovea and hemorrhage. The proposed approach is an unsupervised method and does not demand training phase. Furthermore, the method can be implemented efficiently and can be stopped automatically, the user interaction or adjustment of parameters is not necessary.
资助项目	National Natural Science Foundation of China[11301524] ; National Natural Science Foundation of China[11501540] ; National Natural Science Foundation of China[11471307] ; National Natural Science Foundation of China[61304255] ; West Light Foundation of Chinese Academy of Sciences ; Academician Special Project of Chongqing Basic and Frontier Research Program[cstc2015jcyjys40001] ; Scientific and Technological Project of Chongqing Municipal Education Commission[KJ1401118] ; Scientific and Technological Project of Chongqing Municipal Education Commission[KJ131225] ; Scientific and Technological Project of Chongqing Municipal Education Commission[KJ1501120]
WOS研究方向	Mathematical & Computational Biology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	AMER SCIENTIFIC PUBLISHERS
WOS记录号	WOS:000423786700015
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/6243]
专题	自动推理与认知研究中心
通讯作者	Wang, Weihua
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Automated Reasoning & Cognit, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 3.Chongqing Univ Arts & Sci, Sch Software Engn, Yongchuan 402160, Peoples R China 4.Chongqing Normal Univ, Sch Math Sci, Chongqing 400047, Peoples R China
推荐引用方式 GB/T 7714	Wang, Weihua,Zhang, Jingzhong,Wu, Wenyuan,et al. An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking[J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS,2018,8(2):262-274.
APA	Wang, Weihua,Zhang, Jingzhong,Wu, Wenyuan,&Zhou, Shuang.(2018).An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking.JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS,8(2),262-274.
MLA	Wang, Weihua,et al."An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking".JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 8.2(2018):262-274.