AUTOMATIC DETECTION OF DIABETIC RETINOPATHY THROUGH OPTIC DISC USING MORPHOLOGICAL METHODS

This paper proposes a method for the automatic detection of optic disc in retinal images. In the diagnosis and grading, the essential step is recognition of optic disk for diabetic retinopathy. The analysis of directional cross section profile focused on the local maximum pixel of pre-processed image is realized by the proposed method using optic disc detection. Each profile is implemented by peak detection and property like shape, size and height of the peak are estimated. The statistical measure of the estimated values for the attributes, where the orientation of the cross-section changes the constitute feature used in morphological classification to exclude encourages candidates. The result is to find the patient is affected by diabetics or not.Keywords: Diabetic retinopathy, Optic disk, Naives Bayes algorithm, Local maximum region

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Detection of microaneurysms in retinal images using an ensemble classifier

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur

Computerized Approaches for Retinal Microaneurysm Detection

The number of diabetic patients throughout the world is increasing with a very high rate. The patients suffering from long term diabetes have a very high risk of generating retinal disorder called Diabetic Retinopathy(DR). The disease is a complication of diabetes and may results in irreversible blindness to the patient. Early diagnosis and routine checkups by expert ophthalmologist possibly prevent the vision loss. But the number of people to be screen exceeds the number of experts, especially in rural areas. Thus the computerized screening systems are needed which will accurately screen the large amount of population and identify healthy and diseased people. Thus the workload on experts is reduced significantly. Microaneurysms(MA) are first recognizable signs of DR. Thus early detection of DR requires accurate detection of Microaneurysms. Computerized diagnosis insures reliable and accurate detection of MA's. The paper overviews the approaches for computerized detection of retinal Microaneurysms

An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy

Individuals with diabetes are more likely to develop Diabetic Retinopathy (DR), a chronic ailment that can lead to blindness if left undiagnosed. Early-stage Diabetic Retinopathy (DR) is characterized by Microaneurysms (MA), which appear as tiny red lesions on the retina. This paper investigates a unique approach for the automated early identification of microaneurysms  in eye images. A unique ensemble classifier technique is suggested in this work. Classifiers like SVM, KNN, Decision Tree, and Naïve Bayes are chosen in this study for building an ensemble model. After preprocessing the image, certain common image characteristics such as shape and intensity features were retrieved from the candidate. The mean absolute difference of each feature is computed. Based on mean ranges that would give improved classification results, an expert classifier is chosen and trained. The outputs of the classifiers are integrated for each of the distinct characteristics, and the number of categories that have been most frequently repeated is utilized to reach a final decision. The process has been comprehensively validated using two available open datasets, like e-ophtha and DIARETDB1. On the e-ophtha and DIARETDB1 datasets, the ensemble model achieved an AUC of 0.928 and 0.873, Sensitivity of 90.7% and 85%, Specificity of 90% and 91% respectively

Detection of Glaucoma and Microaneurysm in Retinal Fundus Images Based on Optic Disk and Optic Cup Segmentation

Diabetes cause’s metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. The occurrence of micro aneurysms, a small red dot in retinal layer in the eye, is one of the early signs of diabetic retinopathy. To serve this purpose, various automated systems have been designed which includespreprocessing methods and candidate extractors. The objectives of this paper are to classify different stages of diabetic retinopathy as mild, Moderate, and Severe. Glaucoma is an irreversible chronic eye disease that leads to vision loss. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2-D retinal fundus images. In the proposed method, the optic disc and optic cup is segmented using thresholding algorithm. The cup to disc ratio (CDR) is computed based on the measurement of the segmented optic disc and optic cup. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma for a given patient