A comparative analysis of pre-processing techniques in colour retinal images

Abstract

Diabetic retinopathy (DR) is a chronic disease of the ocular retina, which most of the times is only discovered when the disease is on an advanced stage and most of the damage is irreversible. For that reason, early diagnosis is paramount for avoiding the most severe consequences of the DR, of which complete blindness is not uncommon. Unsupervised or supervised image processing of retinal images emerges as a feasible tool for this diagnosis. The preprocessing stages are the key for any further assessment, since these images exhibit several defects, including non uniform illumination, sampling noise, uneven contrast due to pigmentation loss during sampling, and many others. Any feasible diagnosis system should work with images where these defects were compensated. In this work we analyze and test several correction techniques. Non uniform illumination is compensated using morphology and homomorphic filtering; uneven contrast is compensated using morphology and local enhancement. We tested our processing stages using Fuzzy C-Means, and local Hurst (self correlation) coefficient for unsupervised segmentation of the abnormal blood vessels. The results over a standard set of DR images are more than promising.