{"title":"视网膜病变概率图的自动生成","authors":"J. Rudas, Ricardo Toscano, G. Sánchez","doi":"10.1109/STSIVA.2012.6340548","DOIUrl":null,"url":null,"abstract":"The aim of this paper is the automatic generation of probability lesion maps from a color retinography analysis. A probability map determines the degree of belonging of each pixel in a given set. This process is based on three sequences of procedures: A set of attributes (contrasts enhancement zones), a suppression of artifacts obstacles and a setting of value of each pixel belong to a bright lesion, using probabilistic mapping by supervised classification. The validation of the results was carried out by comparing the subsequent binary map with prior probabilistic diagnosis. The results were analyzed by mean square error (MSE) between resulting diagnosis and diagnostic specialist. MSE was reached of 1.22 on a set of 40 images DIARECTDB1 repository.","PeriodicalId":383297,"journal":{"name":"2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic generation of probability maps of retinal lesions\",\"authors\":\"J. Rudas, Ricardo Toscano, G. Sánchez\",\"doi\":\"10.1109/STSIVA.2012.6340548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is the automatic generation of probability lesion maps from a color retinography analysis. A probability map determines the degree of belonging of each pixel in a given set. This process is based on three sequences of procedures: A set of attributes (contrasts enhancement zones), a suppression of artifacts obstacles and a setting of value of each pixel belong to a bright lesion, using probabilistic mapping by supervised classification. The validation of the results was carried out by comparing the subsequent binary map with prior probabilistic diagnosis. The results were analyzed by mean square error (MSE) between resulting diagnosis and diagnostic specialist. MSE was reached of 1.22 on a set of 40 images DIARECTDB1 repository.\",\"PeriodicalId\":383297,\"journal\":{\"name\":\"2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STSIVA.2012.6340548\",\"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 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STSIVA.2012.6340548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic generation of probability maps of retinal lesions
The aim of this paper is the automatic generation of probability lesion maps from a color retinography analysis. A probability map determines the degree of belonging of each pixel in a given set. This process is based on three sequences of procedures: A set of attributes (contrasts enhancement zones), a suppression of artifacts obstacles and a setting of value of each pixel belong to a bright lesion, using probabilistic mapping by supervised classification. The validation of the results was carried out by comparing the subsequent binary map with prior probabilistic diagnosis. The results were analyzed by mean square error (MSE) between resulting diagnosis and diagnostic specialist. MSE was reached of 1.22 on a set of 40 images DIARECTDB1 repository.
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