Analysis of Diabetic Retinopathy by Automatic Detection of Exudates

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

A Survey on Detection of Macular Retinal Edema

Retinal images of humans play an main role in the detection and diagnosis of many eye diseases for ophthalmologists. Diabetic Retinopathy is a severe and largely spread eye disease which can be regarded as manifestation of diabetes on retina. Retinopathy exactly means damage to retina. There are two types of retinopathy.The most common type is background or non proliferative diabetic retinopathy.A feature extraction technique is introduced to capture the global characteristics of the fundus images and inequity the normal from DME images.Exudates are the primary sign of diabetic retinopathy.So detection of exudates is very important in diagnosis of diabetic retinopathy.While detect the exudates, segmentation of blood vessels in retinal images is necessary

Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

Automatic Classification of Bright Retinal Lesions via Deep Network Features

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

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

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

Ant Colony Optimization Based Exudates Segmentation of Fundus Images

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

Improving the economic value of photographic screening for optical coherence tomography-detectable macular oedema : a prospective, multicentre, UK study

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