An efficient automated method for exudates segmentation using image normalization and histogram analysis

Exudates are one of the abnormalities present on the retina which are used for identification of diseases like Diabetic Retinopathy and Macular Edema. There arises a need for automated and correct segmentation of exudates from digital fundus images. This paper proposes an automated computer vision technique for efficient exudates segmentation from fundus images. The proposed method segments the exudates using an adaptive intensity based threshold which is selected by strategically combining first order statistical parameters and local thresholding based method. The proposed technique correctly detects exudates from the fundus images with an average computation time of 9 seconds. The proposed method is computationally fast and can be used in image processing based applications for diagnosis of ocular diseases.