Ana González, Beatriz Remeseiro, M. Ortega, M. G. Penedo, Pablo Charlón
{"title":"Automatic cyst detection in OCT retinal images combining region flooding and texture analysis","authors":"Ana González, Beatriz Remeseiro, M. Ortega, M. G. Penedo, Pablo Charlón","doi":"10.1109/CBMS.2013.6627825","DOIUrl":null,"url":null,"abstract":"In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.","PeriodicalId":20519,"journal":{"name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","volume":"7 1","pages":"397-400"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2013.6627825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37
Abstract
In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.