{"title":"基于Gabor滤波和人工神经网络的视网膜血管分割","authors":"M. Nandy, M. Banerjee","doi":"10.1109/EAIT.2012.6407885","DOIUrl":null,"url":null,"abstract":"This paper demonstrates an automated segmentation scheme of retinal vasculature using Gabor filter bank, which is optimized on the basis of entropy. Different distributions of filter responses are encoded into features and the vasculature of normal and abnormal retina are segmented by artificial neural network(ANN). The training set of labeled pixels is obtained from the ground truth images of DRIVE database. The Receiver Operating Characteristics (ROC) in both abnormal and normal cases shows 96.16% accuracy.","PeriodicalId":194103,"journal":{"name":"2012 Third International Conference on Emerging Applications of Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Retinal vessel segmentation using Gabor filter and artificial neural network\",\"authors\":\"M. Nandy, M. Banerjee\",\"doi\":\"10.1109/EAIT.2012.6407885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper demonstrates an automated segmentation scheme of retinal vasculature using Gabor filter bank, which is optimized on the basis of entropy. Different distributions of filter responses are encoded into features and the vasculature of normal and abnormal retina are segmented by artificial neural network(ANN). The training set of labeled pixels is obtained from the ground truth images of DRIVE database. The Receiver Operating Characteristics (ROC) in both abnormal and normal cases shows 96.16% accuracy.\",\"PeriodicalId\":194103,\"journal\":{\"name\":\"2012 Third International Conference on Emerging Applications of Information Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third International Conference on Emerging Applications of Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EAIT.2012.6407885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third International Conference on Emerging Applications of Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EAIT.2012.6407885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Retinal vessel segmentation using Gabor filter and artificial neural network
This paper demonstrates an automated segmentation scheme of retinal vasculature using Gabor filter bank, which is optimized on the basis of entropy. Different distributions of filter responses are encoded into features and the vasculature of normal and abnormal retina are segmented by artificial neural network(ANN). The training set of labeled pixels is obtained from the ground truth images of DRIVE database. The Receiver Operating Characteristics (ROC) in both abnormal and normal cases shows 96.16% accuracy.