Exudates Detection Methods in Retinal Images Using Im- age Processing Techniques

Exudates are one of the most common occurring lesions in diabetic retinopathy. Exudates can be identified as areas with hard white or yellowish colors and varying sizes, shapes and locations near the leaking capillaries within the retina. The detection of exudates is the major goal. For this the pre-requisite stage is the detection of optic disc. Once the optic disc is found certain algorithms could be used to detect the presence of exudates. In this paper few methods are used for the detection and the performance of all the methods are compared. —————————— a —————————— 1 I India and China are, and will remain, the leading coun- tries in terms of the number of people with diabetes melli- tus in the year 2025. Among the 10 leading countries in this respect, five are in Asia. Although only a moderate increase in the total population in China is expected in the next 25 years, China is estimated to contribute almost 38 million people to the global burden of diabetes in the year 2025. India, due to its immense population size and high diabetes prevalence, will contribute 57 million (1)and (2). These figures are based on estimated population growth, population ageing, and urbanization, but they do not take into account changes in other diabetes-related risk factors. So, Diabetic screening programmes are necessary in addressing all of these factors when working to eradicate preventable vision loss in diabetic patients. When per- forming retinal screening for Diabetic Retinopathy (3) some of these clinical presentations are expected to be imaged. Diabetic retinopathy is globally the primary cause of blindness not because, it has the highest inci- dence and it often remains undetected until severe vision loss occurs. Advances in shape analysis, the development of strategies for the detection and quantitative characteri- zation of blood vessel changes in the retina are of great importance. Automated early detection of the presence of exudates can assist the ophthalmologists to prevent the spread of disease more efficiently. Direct digital image acquisition using fundus cameras combined with image processing and analysis techniques has the potential to enable automated diabetic retinopathy screening. The normal features of fundus images include optic disk, fovea and blood vessels. Ex- udates and haemorrhages are the main abnormal features which is the leading cause of blindness in the working age population. Optic disk is the brightest (4) part in the normal fundus images which can be seen as a pale, round or vertically slightly oval disk. Finding the main components in the fundus images helps in characterizin g detected lesions and in identifying false positives. Abnormality detection in images is found to play an important role in many real life applications (5) suggested neural network approach for the detection and classification of exudates. A decision support frame work for deducing the presence or absence of DR are developed and tested (6). The detection rule is based on binary-hypothesis testing problem which simpl- ifies the problem to yes/no decisions. The results suggest that by biasing the classifier towards DR detection, it is possible to make the classifier achieve good sensitivity.