An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking
文献类型：期刊论文
| 作者 | Wang, Weihua1,2,3; Zhang, Jingzhong1; Wu, Wenyuan1 ; Zhou, Shuang4
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| 刊名 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
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| 出版日期 | 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 |
| WOS记录号 | WOS:000423786700015 |
| 出版者 | AMER SCIENTIFIC PUBLISHERS |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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