资源类型：
期刊/会议
文章类型：
会议论文
机构：
[a]Department of Mathematics, Beijing University of Chemical Technology, Beijing, China
[b]Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China
临床科室
眼科
首都医科大学附属北京儿童医院
出处：
2014,
关键词：
Computer-aided diagnosis
Fundus images
Glaucoma diagnosis
ISNT rule
Machine learnin
摘要：
Glaucoma is a chronic eye disease which cannot be cured, so that detecting the disease in time is important. Machine learning for glaucoma diagnosis has achieved great development in recent years. In this paper, we present an algorithm for glaucoma diagnosis from optic disc and optic cup boundary lines in fundus images based on doctors' knowledge. We do meticulous division, scaling transformation and principal component analysis on the optic disc and optic cup boundary lines to extract features. The extracted features correspond well with doctors' knowledge. Therefore, we can make an intuitive explanation for the diagnosis results to doctors, rather than just as a black-box prediction. On a real sample set, the proposed feature extraction and diagnosis algorithms achieve high prediction accuracy. © 2014. The authors - Published by Atlantis Press.
第一作者：
Xu, Y
推荐引用方式(GB/T 7714)：
Xu Y,Hu M,Jia X,et al.Computer-aided diagnosis of glaucoma using fundus images[J].2014,
APA：
Xu, Y,Hu, M,Jia, X&Zhao, L.(2014).Computer-aided diagnosis of glaucoma using fundus images.,,
MLA：
Xu, Y,et al."Computer-aided diagnosis of glaucoma using fundus images". .(2014)
