353 research outputs found

    Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

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    Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%.  These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR

    UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

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    One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.Â

    Image Processing Technique for Hard Exudates Detection for diagnosis of Diabetic Retinopathy

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    Diabetic Retinopathy(DR) is a diabetic eye diseases which is referred as combination of various eye problems. These Problems are faced as a complication of diabetes by people, who are suffering from it. Prolongation of DR may result in permanent blindness. To avoid this, Detection of DR in an automated way at early stage is recommended. Hard Exudates are one of the primary abnormalities that can be seen in DR. In this paper, we have given various Image Processing Techniques that can be used for automated detection of Hard Exudates. We have evaluated the outcomes by using ground truth of the test images and the use of image databases in the particular digital algorithm for detection of Hard Exudates. Accuracy, sensitivity and Specificity are few of the parameters which are used for the concluding the better method for digital Processing DOI: 10.17762/ijritcc2321-8169.16047

    Assess the performance of the diagnosis ways of diabetic retinopathy

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    Considered the diagnosis of diseases using image processing is one of the most important areas of image processing techniques used in the medical field, where is the digital data in the field of ophthalmology focus of researchers for automatic detection of some important diseases such as diabetic retinopathy (DR). And is defined as damage to the retina of the eye comes as serious complications and on the human body complications resulting from diabetes in the long term and is considered one of the most important causes of blindness in the world and cause serious damage to the retina. The research aims to Assess the performance of some of the methods used in the diagnosis of diabetic retinopathy by revealing one of the most important accompanying pests him in the retina of the eye and is the exudates and through diagnosed in images digital fundus through image processing techniques where this detection process contributes in helping to early detection

    Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

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    Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification

    Color Feature Segmentation Image for Identification of Cotton Wool Spots on Diabetic Retinopathy Fundus

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    Fundus is an image of the inner eye surface in the form of a colored image. This image has a lot of pixel values because it consists of three basic color components. The three colors are red, green, and blue, so they need a good technique in analyzing this image. This image can be used to diagnose diabetic retinal disease caused by diabetes mellitus. This disease can interfere with human vision because objects that cover the retina of the eye is called Cotton Wool Spot (CWS). The severity of this disease can be observed from the large area of the CWS covering the retina. This study aims to calculate the exact area ratio of CWS with the retina area. The method used in this research is Image Color Feature Segmentation (ICFS). This method has four stages, namely preprocessing, segmentation, feature extraction, and feature areas. The dataset processed in this study was sourced from the Radiology Department, General Hospital of M. Djamil Padang. The dataset consists of 16 fundus images of patients who were treated at the hospital. The results of this study can identify and calculate the percentage of retinal damage is very well. Therefore, this study can be a reference in measuring the severity of diabetic retinopathy for prevention and subsequent treatment for patients and doctors

    Diabetic Retinopathy Exudate Detection

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    The Diabetic Retinopathy is major cause of vision loss now days.High sugar levels in blood can damage the blood vessels that feed the retina of the eye. It contains two types Non-proliferative andProliferative, which results in blurred vision at first and permanent vision loss later. Early detection of Diabetic Retinopathy is helpful to prevent vision loss. Manually detection is laborious process and takes great deal of time for analysis & diagnosis, also it includes chemical dilation which has negative side effects. Amongst all the symptoms like Microaneurysms(small swelling that forms in the walls of tiny blood vessels, which may break & allow blood to leak into nearby tissue.), Hemorrhages(internal bleeding), Exudates (lipid leaks & mark the existence of retinal oedema; known cause for the blindness) is most prevalent symptom, hence will go for detection of exudates
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