Design of Automated Diabetes Screening System on Blurred Retinal Fundus Image

Abstract

Diabetic Retinopathy (DR), a common ocular disease where the retina of human eye is damaged due to fluid leaking from blood vessels of the retina. In extreme case, the patient will become blind. Therefore early detection of DR is crucial to prevent blindness. Microaneurysm (MA), small dark round dots on retinal fundus image is the earliest clinical sign of DR disease. MA detection at early stage can help to reduce the blindness. In such cases the retinal fundus images produced by fluorescent oscilloscope are often noisy and low in contrast. Detecting the Microaneurysms using those fundus images is very difficult for ophthalmologist. In the present paper, we propose a method using location based contrast enhancement process, popularly known as Contrast Limited Adaptive Histogram Equalization (CLAHE) for the detection of retinal changes in DR images. CLAHE is an adaptive extension of Histogram Equalization which helps in dynamic preservation of the local contrast characteristics of an image. The proposed algorithm divides the retinal fundus image into a number of small, non-overlapping contextual tiles. Following CLAHE at each tile separately, median filtering of DR images is carried out in order to smooth the background noise. Results of the proposed algorithm show a considerable improvement in the enhancement of DR image quality. The proposed method tested on publicly available datasets, such as DIARETDB0, DIARETDB1, STARE and DRIVE. The result is also verified by medical practitioners of this field. The proposed technique has been tested on 45 images collected from local hospital. Mean Sensitivity of 84.48% and mean Accuracy of 98.94% has been obtained.