1,582 research outputs found

    Analysis of Diabetic Retinopathy by Automatic Detection of Exudates

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    ABSTRACT In this research paper, a method for automatic detection of Exudates in digital eye fundus image is described. To develop an automated diabetic retinopathy screening system, a detection of dark lesions in digital fundus photographs is needed. Exudates are the first clinical sign of diabetic retinopathy and they appear small white dots on retinal fundus images. The number of Exudates is used to indicate the severity of the disease. Early Exudates detection can help reduce the incidence of blindness. Here, a method for the automatic detection of Diabetic Retinopathy (ADDR) in color fundus images was analyzed. Different preprocessing, feature extraction and classification algorithms are used. The performance of the automated system is assessed based on Sensitivity and Specificity

    Ant Colony Optimization Based Exudates Segmentation of Fundus Images

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    Now a days, Diabetic Retinopathy is a deadly form of disease. Diabetic retinopathy is a complication of diabetes and a leading cause of blindness. It occurs when diabetes damages the tiny blood vessels inside the retina, the light-sensitive tissue at the back of the eye. Exudates of diabetic retinopathy appears as white or yellow in color. Early detection of diabetic retinopathy is not possible as patients are generally asymptomatic.  Exudates are frequently observed with microaneurysms. These methods are noise presence, low contrast, uneven illumination, and color variation. Therefore, in order to overcome the above stated issues computer aided diagnosis for exudates segmentation is needed. This proposed system first preprocesses the fundus image of human retina which is followed by image segmentation in which exudates are segmented. Proposed study segments the exudates using Ant Colony optimization Algorithm. The algorithm’s performance was evaluated with a dataset available online. Classification is performed on segmented image to classifying the image as Normal retina and diabetic retinopathy retina

    Automatic Classification of Bright Retinal Lesions via Deep Network Features

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    The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017

    A Novel Approach for the Detection & Classification of Diabetic Retinopathy

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    When sugar level(glucose) in the blood fails to regulate the insulin properly in human body ,diabetic is occurred. The effect of diabetic on eye causes diabetic retinopathy. Diabetic Retinopathy is one of a complicated diabetes which can cause blindness .It is a metabolic disordered. patients perceive no symptoms until the disease is at late stage. So early detection and proper treatment has to be ensured. To serve this purpose, various automated systems have been designed. There are two levels of diabetic retinopathy which are non proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR).The presence of micro aneurysms in the eye is one of the early signs of diabetic retinopathy. The objectives of this paper are 1) classify different stages of non proliferative diabetic retinopathy (NPDR) as mild NPDR, Moderate NPDR, Severe NPDR. 2)classification of micro aneurysms and exudates. DOI: 10.17762/ijritcc2321-8169.15031

    Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

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    Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

    Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression

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    Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación; EcuadorFil: Brun, Marcel. Universidad Nacional de Mar del Plata; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentin
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