NEW SEGMENTATION METHODOLOGY FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGES

In this work, we developed an approach based on mathematical morphology and the k-means clustering algorithm to detect hard exudates (HEs) in images taken by retinography from different diabetic patients. The presence of exudates within the macular region is a hallmark of diabetic macular edema and is detected by diagnostics with high sensitivity. In ophthalmologic images, the segmentation of HEs is essential to characterize the shape of the lesion for analysis. In this domain, several approaches have been employed for exudate extraction. Some authors have used only the mathematical morphology, but this approach does not provide very good detection of exudates. In this paper, we combined the k-means clustering algorithm and the mathematical morphology. This approach was tested on a set of 50 ophthalmologic images. The obtained results were compared with manual segmentation by an ophthalmologist.