Detecção de exsudatos em imagens de retina por técnicas de morfologia matemática e agrupamento nebuloso

Diabetic retinopathy (DR) is one of the major complications of diabetes mellitus and, furthermore it causes severe damage to the retina and consequently to the vision. DR may lead to blindness and therefore it is important to prevent it or early detect and treat it. The diagnosis of DR is performed by visual analysis of retinal images being exudates (fat deposits) the main patterns traced by a specialist doctor. It is noteworthy that early diagnosis, through regular monitoring when coupled with proper treatment, results in numerous benefits in the prevention of visual impairment. Thus, this paper proposes an algorithm for exudate detection in retinal images, whose experimental validation is performed on retina images of the publicly available DIARETDB1 database. The reason for choosing this database is that it provides spatial coordinates of exudates in retina images which constitute ground truths for the algorithm validation. The proposed methodology combines fuzzy clustering and mathematical morphology techniques, and thus it provides a method for optic disk detection considering that it is as the convergent point of vessels. The exudate detection method presented successful rates of 73.03% and 99.41% concerning the use of the whole image and only partial regions, respectively. These results confirm the performance improvement provided by the proposed methodology, when comparing it to other methods available in the literature.