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.
语种：
外文
WOS：
第一作者：
第一作者机构：[1]Beijing Univ Chem Technol, Dept Math, Beijing, Peoples R China;
通讯作者：
通讯机构：[1]Beijing Univ Chem Technol, Dept Math, Beijing, Peoples R China;
推荐引用方式(GB/T 7714)：
Yongli Xu,Xin Jia,Man Hu,et al.Computer-aided Diagnosis of Glaucoma Using Fundus Images[J].PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MECHATRONICS, ELECTRONIC, INDUSTRIAL AND CONTROL ENGINEERING.2014,5:925-+.
APA：
Yongli Xu,Xin Jia,Man Hu&Lina Zhao.(2014).Computer-aided Diagnosis of Glaucoma Using Fundus Images.PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MECHATRONICS, ELECTRONIC, INDUSTRIAL AND CONTROL ENGINEERING,5,
MLA：
Yongli Xu,et al."Computer-aided Diagnosis of Glaucoma Using Fundus Images".PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MECHATRONICS, ELECTRONIC, INDUSTRIAL AND CONTROL ENGINEERING 5.(2014):925-+
