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This paper proposes a framework for detecting the presence and assessing the severity of exudate formations in patients suffering from diabetic retinopathy. The proposed framework also localizes and detects the fovea, the knowledge of which aids in determining the severity of the impairment to visual function posed by the exudates. Results are presented using digital fundus images collected from both...
Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression...
Retinal images are used for the automated diagnosis of diabetic retinopathy. The retinal image quality must be improved for the detection of features and abnormalities and for this purpose segmentation of retinal images is vital. In this paper, we present a novel automated approach for segmentation of colored retinal images. Our segmentation technique smoothes and strengthens images by separating...
Red lesions in the form of Microaneurysms (MAs) and Hemorrhages (HMs) are among the first explicit signs of diabetic retinopathy (DR). Hence robust detection of these lesions is an important diagnostic task in computer assistance systems. In this paper we present a new curvelet based algorithm to separate these red lesions from the rest of the color retinal image. In order to prevent fovea to be considered...
Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated late. In its earliest stage, DR can be diagnosed by micro aneurysm (MA). Although some algorithms have been developed, the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method to detect MA based on Sparse Representation...
People with diabetes may face eye problem as a complication of diabetes. These eye problems can cause vision loss and even blindness. There are several lesions that appear such microaneurysms, hemorrhages, cotton wool spots and exudates. Exudates tend to form ring, around area of diseased vessel and appeared as yellowish-white deposits with well-defined edges meanwhile cotton wool spots are grayish-white...
Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This paper presents an automated method for the detection of bright lesions (exudates) in retinal images. In this work, an adaptive thresholding based on a novel algorithm for pure splitting of the image is proposed. A coarse...
Precise pupil features detection is an important factor for screening diabetic retinopathy. Some interferences caused by reflections, eyelashes and eyelids in pupil extraction need to be solved. This paper presents an algorithm to precisely estimate pupil features: pupil center and pupil radius. The system's hardware component allows for high frame rate image acquisition under infrared lighting conditions...
Microaneurysms (MAs) are the earliest sign of diabetic retinopathy and manifest as small reddish spots on the retina. Generally, algorithm design for MAs detection starts by separating the vascular system from the background for a posterior analysis of candidate MAs presence. Following this approach, this paper assesses three different methods for vessel segmentation and how they affect posterior...
Exudates are a class of lipid retinal lesions visible through optical fundus imaging, and indicative of diabetic retinopathy. We propose a clustering-based method to segment exudates, using multi-space clustering, and colorspace features. The method was evaluated on a set of 89 images from a publicly available dataset, and achieves an accuracy of 89.7% and positive predictive value of 87%.
Retinal images are used for automated diagnosis of Diabetic Retinopathy. Preprocessing of retinal image is required prior to detection of features and abnormalities. The objective of preprocessing segmentation is to separate the background and noisy area from the overall image to enhance the quality of acquired retinal image. We present a method for colored retinal image preprocessing and enhancement...
The optic disc (OD) and exudates form the main features of fundus images for diagnosing eye disease such as diabetic retinopathy and glaucoma. In this paper, an algorithm for the extraction of OD and exudates from fundus images based on marker controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps before...
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