Title :
Automatic detection of glaucoma in retinal images
Author :
Li Xiong ; Huiqi Li ; Yan Zheng
Author_Institution :
Beijing Inst. of Technol., Beijing, China
Abstract :
A new method to detect glaucoma is proposed in this paper, which is based on principle components analysis (PCA) and Bayes classifier. Firstly, optic disc center is located using the combination of thresholding and distance transformation. Eigenvector spaces of normal set and glaucoma set are obtained respectively using PCA. A test image is projected onto these two spaces and the distance between projection and each template is calculated. Finally, decision is made according to Bayes classifier. The success rate of optic disk localization is 95.3% and 89.9% for normal set and glaucoma set respectively. The glaucoma detection algorithm was tested by over three hundred retinal images and the success rate is 78%.
Keywords :
Bayes methods; biomedical optical imaging; eye; image classification; medical disorders; medical image processing; principal component analysis; vision defects; Bayes classifier; PCA; distance transformation; eigenvector spaces; glaucoma automatic detection; optic disc center; principal component analysis; retinal images; thresholding; Optical fiber sensors; Optical fibers; Optical imaging; Retina; Training; Bayes Classifier; Distance Transformation; Glaucoma; PCA;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
DOI :
10.1109/ICIEA.2014.6931312
