Detection of Ocular Pathologies From Iris Images Using Blind De-Convolution and Fuzzy C-Means Clustering

Abstract

The images of disease-affected and normal eyes collected from high-resolution fundus (HRF) image database are analyzed, and the influence of ocular diseases on iris using a reliable fuzzy recognition scheme is proposed. Nearly 45 samples of iris images are acquired using Canon CR-1 fundus camera with a field of view of 45° when subjected to routine ophthalmology visits, and the samples of eye images include healthy eyes, eyes affected by glaucoma, cataract, and diabetic retinopathy. These images are then subjected to various image processing techniques like pre-processing for de-noising using blind de-convolution, wavelet-based feature extraction, principal component analysis (PCA) for dimension reductionality, followed by fuzzy c-means clustering inference scheme to categorize the normal and diseased eyes. It is inferred that the proposed method takes only two minutes with an accuracy, specificity, and sensitivity varying in the range of 94% to 98%, respectively.