Diabetic retinopathy, the most common diabetic eye disease, occurs when blood vessels in the retina change. Sometimes these vessels swell and leak fluid or even close off completely. In other cases, abnormal new blood vessels grow on the surface of the retina. Early detection can potentially reduce the risk of blindness. This paper presents an automated method for the detection of exudates in retinal color fundus images with high accuracy, First, the image is converted to HSI model, after preprocessing possible regions containing exudate, the segmented image without Optic Disc (OD) using algorithm Graph cuts, Invariant moments Hu in extraction feature vector are then classified as exudates and non-exudates using a Neural Network Classifier. All tests are applied on database DIARETDB1.
<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>
Digital images of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The localization of the Optic Disc (OD) center is the first and necessary step identification and segmentation of anatomical structures and in pathological retinal images. From the center of the optic disc spreads the major blood vessels of the retina. Therefore, by considering the high number of vessels and the high number of the angles resulted from the vessels crossing, the authors propose a new method based on the number of angles in the vicinity of optic disc for localization of the center of optic disc. The first step is pre-processing of retinal image for separate the fundus from its background and increase the contrast between contours. In the second step, the authors use the Curvature Scale Space (CSS) for angle detection. In the next step, they move a window about the size of optic disc to count the number of corners. In the final step, they use the center of windows which has the most number of corners for localizing the optic disc center. The proposed method is evaluated on DRIVE, CHASE_DB1 and STARE databases and the success rate is 100, 100 and 96.3%, respectively.
Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.
Abstract-This paper provides a review of methods advanced in the past few years for segmentation of color images. After a brief definition of the segmentation, we outline the various existing techniques, classified according to their approaches. We have identified five that are based approaches contours, those relying on notion of region, structural approaches, those based on the form and then using those notions of graphs. For each of these approaches, we then explained and illustrated their most important methods. This review is not intended to be exhaustive and the classification of certain methods may be discussed since at the boundary between different approaches.
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