Automatic Detection of Diabetic Retinopathy from Color Fundus Retinal Images

The influence and impact of digital images on modern society is tremendous, and image processing is now a critical component in science and technology, in which Image segmentation plays a crucial role in many medical imaging applic ations. Medical image segmentation has a vital role in diagnosis, surgical planning, navigation, and various medical evaluations. Moreover it is suitable for segmenting the blood vessel of retinal images which is used for automated screening of early diabe tic retinopathy (damage to the retina) detection caused by complications of diabetes mellitus, which can eventually lead to blindness. One of the main challenges in medical image processing is to segment the blood vessel with higher accuracy rate hence we propose a novel technique to increase the accuracy rate of segmenting the blood vessel

Diabetes and Hard Disease Detection Algorithm

In diebetise detection we are using a detection technique using an algorithm based on k map technique this technique is helpful for the detection of disease in effective manner and smooth way. The k map method has two comparative way which has to be compared and get result in the form of graph or table .this algorithm is to be achieved in R programming

Diabetes and Hard Disease Detection Algorithm

diebetise detection we are using a detection technique using an algorithm based on k map technique this technique is helpful for the detection of disease in effective manner and smooth way. The k map method has two comparative way which has to be compared and get result in the form of graph or table .this algorithm is to be achieved in R programming

Feature Selection Method for Iris Recognition Authentication System

Iris-based biometric authentication is gaining importance in recent times. Iris biometric processing however, is a complex process and computationally very expensive. In the overall processing of iris biometric in an iris-based biometric authentication system, feature selection is an important task. In feature selection, we ex-tract iris features, which are ultimately used in matching. Since there is a large number of iris features and computational time increases as the number of features increases, it is therefore a challenge to develop an iris processing system with as few as possible number of features and at the same time without compromising the correctness. In this paper, we address this issue and present an approach to feature Selection Method

Implementation of Pre-processing and Efficient Blood Vessel Segmentation in Retinopathy Fundus Image

The human retina is a light receptive tissue and its enormously rich in blood vessels for its high physiological stress and dysfunction of the retinal vasculature can effect from several diseases. Diabetic retinopathy is caused due to complications of diabetes, which can eventually develop new blood vessels at the back of the retina and it can lead to blur vision or loss of vision. This work describes the problems of retinopathy associated with diabetic patients and premature babies. We propose methods for the preprocessing and efficient segmentation method to support measurement of the openness of the MTA, including image enhancement techniques like morphological operations, efficient luminance component construction and bank of Gabor filters to segment retinal blood vessels. Finally an image cropping is used to separate inferior and superior part of this segmented image for the effective and detailed analysis of the vascular structure in the fundus eye images. Certain retinal disorders, if not detected in time, can cause serious problems like blur vision and blindness in patients. The implementation and the performance of the various edge detection methods like Canny, Sobel and Gabor filters are based on visual perception. It has been concluded that in case of natural images such as retinal fundus image a Gabor filter yielded better results in segmentation of blood vessels as compared to edge detection methods of Canny and Sobel. DOI: 10.17762/ijritcc2321-8169.15066

Exudate segmentation using fully convolutional neural networks and inception modules

Diabetic retinopathy is an eye disease associated with diabetes mellitus and also it is the leading cause of preventable blindness in working-age population. Early detection and treatment of DR is essential to prevent vision loss. Exudates are one of the earliest signs of diabetic retinopathy. This paper proposes an automatic method for the detection and segmentation of exudates in fundus photographies. A novel fully convolutional neural network architecture with Inception modules is proposed. Compared to other methods it does not require the removal of other anatomical structures. Furthermore, a transfer learning approach is applied between small datasets of different modalities from the same domain. To the best of authors’ knowledge, it is the first time that such approach has been used in the exudate segmentation domain. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed algorithm accomplished better results using a diseased/not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications

Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy

Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques.