Automatic detection of exudates in retinal images using histogram analysis

Diabetic Retinopathy (DR) is the major cause of blindness caused by the damage to the blood vessels in the retina from diabetes. It cannot be prevented but early detection through fundus imaging by an ophthalmologist can prevent further vision loss. Presence of microaneurysms, hemorrhages, cotton-wool spots and exudates are the symptoms of mild DR. Of these, the detection of exudates is one of the important factors in the early diagnosis of DR. Exudates are fatty deposits on the retina which appear as yellowish regions in fundus image. Fundus images show considerable variation in brightness which makes automatic detection of exudates difficult. In this study, we are proposing a new method for preprocessing and false positive elimination towards the reliable detection of exudates. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. To emphasize brighter yellow regions (exudates), gamma correction was performed on each red and green components of the image. Subsequently, the histograms of each red and green component were extended. After that, the exudates candidates were detected using histogram analysis. Finally, false positives were removed by using multi-channel histogram analysis. To evaluate the new method for the detection of exudates, we examined 158 fundus images, including 84 abnormal images with exudates and 74 normal images. The sensitivity and specificity for the detection of abnormal and normal cases were 88.45% and 95.5% respectively.