International Journal of Innovative Research and Development

Deepashree Devaraj , Nagaveena zzz

Abstract

Microaneurysm in the retina is one of the early signs of diabetic retinopathy. The recognition of Microaneurysms is an essential and the most challenging tasks for reliable automatic detection and diagnosis of diabetic retinopathy. This helps ophthalmologist to detect the emergence of its early symptoms and determine the next action step for the patient. A number of studies have been investigated on retinal FUNDUS images for diabetic retinopathy. This paper deals with the recently introduced methods for analyzing the microaneurysms detection, available for automated diagnosis. Although many algorithms have been developed, the accurate detection of microaneurysm in color retinal images is still a challenging problem. Automating this task, which is currently performed manually, would bring more objectivity and reproducibility.

Keywords

FUNDUS Image, Diabetic Retinopathy (DR), Microaneurysm, Nonproliferative Diabetic Retinopathy (NPDR), Proliferative Diabetic Retinopathy (PDR)

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Deepashree Devaraj , Manisha ZZZ

Abstract

Diabetic retinopathy is a complication of diabetes that is caused by changes in the blood vessels of the retina. Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This paper presents an automated method for the detection of exudates in retinal images with high accuracy. First, the image is converted to HSI model. After pre processing possible regions containing exudates using gray scale morphology are identified. Then descriptors for each candidate pixel to classify them are extracted. Using Naive-Bayes classifier, Diabetic retinopathy is classified.

Keywords

Diabetic Retinopathy, Retinal Image, Exudates, Morphology, Pre Processing

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