{"title":"利用人工神经网络对在线和局部数据集进行视盘分割","authors":"M. Nandy, Minakshi Banejee","doi":"10.1109/ICICPI.2016.7859717","DOIUrl":null,"url":null,"abstract":"There is a requirement for optic disc dataset with manual segmentation delimited by ophthalmologist and ground truth to compare segmented image obtained by pixel classification method with its ground truth. We tried to address this requirement by collecting some retinal images from local eye clinic and made optic disc marked by ophthalmologist. A widely used online dataset images are also marked by ophthalmologist for optic disc. The ground truths for those images are prepared. This paper presents optic disc localization and segmentation of retinal images obtained from local as well as online public dataset by Artificial Neural network, the results of which are quite satisfactory.","PeriodicalId":6501,"journal":{"name":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","volume":"46 1","pages":"278-282"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optic disc segmentation of both online and local dataset using artificial neural network\",\"authors\":\"M. Nandy, Minakshi Banejee\",\"doi\":\"10.1109/ICICPI.2016.7859717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is a requirement for optic disc dataset with manual segmentation delimited by ophthalmologist and ground truth to compare segmented image obtained by pixel classification method with its ground truth. We tried to address this requirement by collecting some retinal images from local eye clinic and made optic disc marked by ophthalmologist. A widely used online dataset images are also marked by ophthalmologist for optic disc. The ground truths for those images are prepared. This paper presents optic disc localization and segmentation of retinal images obtained from local as well as online public dataset by Artificial Neural network, the results of which are quite satisfactory.\",\"PeriodicalId\":6501,\"journal\":{\"name\":\"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)\",\"volume\":\"46 1\",\"pages\":\"278-282\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICPI.2016.7859717\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICPI.2016.7859717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optic disc segmentation of both online and local dataset using artificial neural network
There is a requirement for optic disc dataset with manual segmentation delimited by ophthalmologist and ground truth to compare segmented image obtained by pixel classification method with its ground truth. We tried to address this requirement by collecting some retinal images from local eye clinic and made optic disc marked by ophthalmologist. A widely used online dataset images are also marked by ophthalmologist for optic disc. The ground truths for those images are prepared. This paper presents optic disc localization and segmentation of retinal images obtained from local as well as online public dataset by Artificial Neural network, the results of which are quite satisfactory.