{"title":"Automatic optic disc detection through background estimation","authors":"Shijian Lu, Joo-Hwee Lim","doi":"10.1109/ICIP.2010.5653473","DOIUrl":null,"url":null,"abstract":"This paper presents an automatic optic disc (OD) detection technique. Given a retinal image, the proposed method first estimates a retinal background surface through an iterative Savitzky-Golay smoothing procedure. The OD is then detected through the global thresholding of the difference between the retinal image and the estimated background surface. Finally, an OD boundary is determined after a pair of morphological post-processing operations. The proposed technique has been tested over three public datasets that are composed of 130, 89, and 40 retinal images, respectively. Experiments show that an average OD detection accuracy of 96.91% is attained. In addition, 84.37% OD pixels are correctly located compared with the manually labeled ones.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2010.5653473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
This paper presents an automatic optic disc (OD) detection technique. Given a retinal image, the proposed method first estimates a retinal background surface through an iterative Savitzky-Golay smoothing procedure. The OD is then detected through the global thresholding of the difference between the retinal image and the estimated background surface. Finally, an OD boundary is determined after a pair of morphological post-processing operations. The proposed technique has been tested over three public datasets that are composed of 130, 89, and 40 retinal images, respectively. Experiments show that an average OD detection accuracy of 96.91% is attained. In addition, 84.37% OD pixels are correctly located compared with the manually labeled ones.