Vision machine learning for detection of ocular pathologies from iris images

Abstract

Detection of eye pathologies from the database of iris images is taken into deliberation. The images of disease affected and normal eyes are collected from High-Resolution Fundus (HRF) Image Database is analyzed and the influence of ocular diseases on iris using a reliable Fuzzy recognition scheme is proposed here. These eye 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 2 minutes with an accuracy, specificity and sensitivity varying in the range of 94% to 98% respectively.

Subject Classification:

• 68T10

Keywords:

• Eye disease
• Blind de-convolution
• Wavelet transform
• Principal Component analysis and fuzzy C-means clustering algorithm