Detection of exudates from digital fundus images of diabetic retinopathy patients

Diabetes is a condition where the body does not produce enough insulin to convert sugar to energy, leading to a build up of sugar in the blood. This leads to a number of problems, including diabetic retinopathy. Diabetic retinopathy is a complication of diabetes that causes damage to the blood vessels of the retina that allowing you to see fine detail. It causes progressive damage to the retina. One of the earliest and most common symptoms of exudate diseases leading to blindness such as diabetic retinopathy and macular degeneration. Some areas of the retina with these conditions must be photocoagulated by laser to stop the progression of the disease and prevelant diseases. Deliminating these areas depends on the delineation of the lesions and anatomical structures of the retina. In this thesis we proposes a simple yet an efficient approach for automatic detection of the exudates of the Diabetic Retinopathy. The detection of exudates of diabetic retinopathy is composed of four main steps: 1. Max filtering of the fudus image converted to grayscale. 2. Fitting a polynomial curve composed of three line segments to the cumalative histogram and specified the second break level as a threhold level 3. Removing optic disk and false exudate regions from the image 4. Finally thresholding the image in the determined regions to get exudates. After exudate detection statistics of exudates have also been computed. The main contribution of this thesis is the automatic threshold level specification approach. The method is verified by an expert and it is seen that the proposed method is promising.