Abstract—An automatic method to detect hard exudates, a lesion associated with diabetic retinopathy, is proposed. The algorithm found on their color, using a statistical classification, and their sharp edges, applying an edge detector, to localize them. A sensitivity of 79.62% with a mean number of 3 false positives per image is obtained in a database of 20 retinal image with variable color, brightness and quality. In that way, we evaluate the robustness of the method in order to make adequate to a clinical environment. Further efforts will be done to improve its performance. Keywords— Diabetic retinopathy, hard exudates, image processing, retinal images. I. INTRODUCTION D IA th he lead BETIC retinopathy (DR) is a severe eye disease at affects many diabetic patients. It remains one of t ing causes of blindness and vision defects in developed countries. There exist effective treatments that inhibit the progression of the disease provided that it would be diagnosed early enough. But DR is usually asymptomatic in its beginning, so diabetic patients do not undergo any eye examination until it is already too late for an optimal treatment and severe retinal damages have been caused. Regular retinal examinations for diabetic patients guarantee an early detection of DR reducing significantly the incidence of blindness cases. Because of great prevalence of diabetes, mass screening is time consuming and requires many trained graders to examine the fundus photographs searching retinal lesions. A reliable method for automated assessment of the presence of lesions in fundus images will be a valuable tool in assisting the limited number of professional and reducing the examination time. This paper focuses only in the automatic detection of one of the lesions associated with DR: hard exudates. They usually appear in the fundus photographs as small yellow- white patches with sharp margins and different shapes. Among lesions caused by DR, exudates are one of the most occurring early lesions [1]. So the detection and quantification of them will contribute to the mass screening and assessing of DR. Some investigations in the past have identified retinal exudates in fundus images based on their gray level [2], [3], their high contrast [4-7] or their color [8],[9]. Because the brightness, contrast and color of exudates vary a lot among different patients and, therefore, different photographs, these method would not work in all the images used in clinical environment. The main improvement introduced by the technique described in this paper is its robustness to the variable appearance of retinal fundus images to obtain an optimal performance in all types of images, in contrast to these other approaches. Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathy C. I. Sánchez 1,2 , R. Hornero 1,2 , M. I. López 2 , J. Poza 1,2 1 Dep. de Teoría de la Señal y Comunicaciones, ETSI-Telecomunicación, University of Valladolid, Spain. 2 Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Spain. II. METHODOLOGY The method attempts to detect hard exudates using two features of this lesion: its color and its sharp edges. So hard exudates extraction is carried out in the following stages: Detection of the optic disk and the blood vessels Detection of yellowish objects in the image. Detection of objects in the image with sharp edges. Combination of the previous steps to detect yellowish objects with sharp edges. A. Detection of the optic disk and the blood vessels In order to localize these main features, we build on some works developed by other authors. We follow the method proposed in [7] to detect the center of the optic disk (OD). This method determined a number of candidate regions with the brightest pixels in intensity image. Then the PCA based model approach is applied to the candidate regions to give the final location of the OD. We also detect the disk boundary using a snake driven by an external field v(x,y)=[u(x,y),v(x,y)] called Gradient Vector Flow (GVF) [10] over the image f(x,y). dxdy f f y v x v y u x u 2 2 ) 2 2 2 2 ( v (1) In this work the snake is initialized automatically as a circle placed in the center of the OD localized previously. The blood vessels are segmented applying the matched filter method described in [11] to enhance blood vessels and thresholding the image obtained. B. Detection of yellowish objects The detection of this kind of objects is carried out performing color segmentation based on the statistical classification method described in [8] and [9]. This method found on the fact that if a group of features can be defined so that the objects in an image map to nonintersecting classes in the feature space, then we can easily identify different objects classifying them into corresponding classes 0-7803-8439-3/04/$20.00©2004 IEEE 1624 Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA • September 1-5, 2004