Mukund Desai, R. Mangoubi, J. Danko, L. Aiello, L. Aiello, Jennifer K. Sun, J. Cavallerano
{"title":"Retinal venous caliber abnormality: Detection and analysis using matrix edge fields-based simultaneous smoothing and segmentation","authors":"Mukund Desai, R. Mangoubi, J. Danko, L. Aiello, L. Aiello, Jennifer K. Sun, J. Cavallerano","doi":"10.1109/AIPR.2009.5466311","DOIUrl":null,"url":null,"abstract":"We present a novel approach for detecting and analyzing Retinal Venous Caliber Abnormalities (VCAB). We use 1) the noise adaptive Matrix Edge Field variational energy functional formulation for simultaneous smoothing and segmentation, and 2) analyze its output, the edge field, to demonstrate the ability to recognize the deformations. This contribution is one step towards a wider vision of establishing an automated, low cost, easy to use classification and decision support system for rapid, accurate, and consistent retinal heath monitoring and lesion detection and classification.1","PeriodicalId":266025,"journal":{"name":"2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2009.5466311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
We present a novel approach for detecting and analyzing Retinal Venous Caliber Abnormalities (VCAB). We use 1) the noise adaptive Matrix Edge Field variational energy functional formulation for simultaneous smoothing and segmentation, and 2) analyze its output, the edge field, to demonstrate the ability to recognize the deformations. This contribution is one step towards a wider vision of establishing an automated, low cost, easy to use classification and decision support system for rapid, accurate, and consistent retinal heath monitoring and lesion detection and classification.1