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

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

A Review of Algorithms for Retinal Vessel Segmentation

oai:ojs.pkp.sfu.ca:article/41This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma.  To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment

Retinal Blood Vessels Extraction Based on Curvelet Transform and by Combining Bothat and Tophat Morphology

Retinal image contains vital information about the health of the sensory part of the visual system. Extracting these features is the first and most important step to analysis of retinal images for various applications of medical or human recognition. The proposed method consists of preprocessing, contrast enhancement and blood vessels extraction stages. In preprocessing, since the green channel from the coloured retinal images has the highest contrast between the subbands so the green component is selected. To uniform the brightness of image adaptive histogram equalization is used since it provides an image with a uniformed, darker background and brighter grey level of the blood vessels. Furthermore Curvelet transforms is used to enhance the contrast of an image by highlighting its edges in various scales and directions. Eventually the combination of Bothat and Tophat morpholological function followed by local thresholding is provided to classify the blood vessels. Hence the retinal blood vessels are separated from the background image.DOI:http://dx.doi.org/10.11591/ijece.v4i3.632

GGM classifier with multi-scale line detectors for retinal vessel segmentation

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation

Towards Complete Ocular Disease Diagnosis in Color Fundus Image

Non-invasive assessment of retinal fundus image is well suited for early detection of ocular disease and is facilitated more by advancements in computed vision and machine learning. Most of the Deep learning based diagnosis system gives just a diagnosis(absence or presence) of a certain number of diseases without hinting the underlying pathological abnormalities. We attempt to extract such pathological markers, as an ophthalmologist would do, in this thesis and pave a way for explainable diagnosis/assistance task. Such abnormalities can be present in various regions of a fundus image including vasculature, Optic Nerve Disc/Cup, or even in non-vascular region. This thesis consist of series of novel techniques starting from robust retinal vessel segmentation, complete vascular topology extraction, and better ArteryVein classification. Finally, we compute two of the most important vascular anomalies-arteryvein ratio and vessel tortuosity. While most of the research focuses on vessel segmentation, and artery-vein classification, we have successfully advanced this line of research one step further. We believe it can be a very valuable framework for future researcher working on automated retinal disease diagnosis

Feature Extraction for Retina Image Based on Difference Approaches

Automatic disease diagnosis using biometric images is a difficult job due to image distortion, such as the presence of artifacts, less or excessive light, narrow vessel visibility and differences in inter-camera variability that affect the performance of an approaches. Almost all extraction methods in the blood vessels in the retina produce the main part of the vessel with no patalogical environment. However, an important problem for this method is that extraction errors occur if they are too focused on the thin vessels, the wide vessels will be more detectable and also artificial vessels that may appear a lot. In addition, when focusing on a wide vessel, the extraction of thin vessels tends to disappear and is low. Based on our research, different treatments are needed to extract thin vessels and wide vessels both visually and in contrast. This study aims to adopt feature extraction strategies with different techniques. The method proposed in segmentation and extraction with three different approaches, namely the pattern of shape, color, and texture. Testing segmentation and feature extraction using STARE datasets with five classes of diseases namely Choroidal Neovascularization, Branch Retinal Vein Occlusion, Histoplasmosis, Myelinated Nerve Fibers, and Coats. Image enhancement on Myelinated Nerve disease Fiber is the best result from the image of other diseases with the highest value of PSNR of 35.4933 dB and the lowest MSE of 0.0003 means that the technique is able to repair objects well. The main significance of this work is to increase the quality of segmentation results by applying the Otsu method. Testing of segmentation results shows improvements with the proposed method compared to other methods. Furthermore, the application of different feature extraction methods will get information on disease classification features based on patterns of suitable shapes, colors, and textures

Robust Retinal Vessel Segmentation using ELM and SVM Classifier

The diagnosis of retinal blood vessels is of much clinical importance, as they are generally examined to evaluate and monitor both the ophthalmological diseases and the non-retinal diseases. The vascular nature of retinal is very complex and the manual segmentation process is tedious. It requires more time and skill. In this paper, a novel supervised approach using Extreme Learning Machine (ELM) classifier and Support Vector Machine (SVM) classifier is proposed to segment the retinal blood vessel. This approach calculates 7-D feature vector comprises of green channel intensity, Median-Local Binary Pattern (M-LBP), Stroke Width Transform (SWT) response, Weber�s Local Descriptor (WLD) measure, Frangi�s vesselness measure, Laplacian Of Gaussian (LOG) filter response and morphological bottom-hat transform. This 7-D vector is given as input to the ELM classifier to classify each pixel as vessel or non-vessel. The primary vessel map from the ELM classifier is combined with the ridges detected from the enhanced bottom-hat transformed image. Then the high-level features computed from the combined image are used for final classification using SVM. The performance of this technique was evaluated on the publically available databases like DRIVE, STARE and CHASE-DB1. The result demonstrates that the proposed approach is very fast and achieves high accuracy about 96.1% , 94.4% and 94.5% for DRIVE, STARE and CHASE-DB1 respectively

Retinal Fundus Anjiyografi Görüntülerinde Drusen Alanlarının Otomatik Tespiti ve Hesaplanması

Computer aided detection (CAD) systems are widely used in the analysis of biomedical images. In this paper, we present a novel CAD system to detect age-related macular degeneration (ARMD) on retinal fundus fluorescein angiography (FFA) images, and we provide an areal size calculation of pathogenic drusen regions. The purpose of this study is to enable identification and areal size calculation of ARMD-affected regions with the developed CAD system; hence, we aim to discover the condition of the disease as well as facilitate long-term patient follow-up treatment. With the aid of this system, assessing the marked regions will take less time for ophthalmologists and observing the progress of the treatment will be a simpler process. The CAD system consists of four stages, a) preprocessing, b) segmentation, c) region of interest detection and d)feature extraction and drusen area detection. Detection through CAD and calculation of drusen regions were performed with a dataset composed of 75 images. The results obtained from the developed CAD system were examined by a specialist ophthalmologist, and the performance criteria of the CAD system are reported as conclusions. As a result, with 66 correct detections and 9 incorrect detections, the developed CAD system achieved an accuracy rate of 88%.Bilgisayar destekli tespit (BDT) sistemleri biyomedikal görüntülerin analizinde geniş bir kullanım alanına sahiptir. Bu çalışmada retinal fundus anjiyografi görüntüleri üzerinde yaşa bağlı makula dejenerasyonu (YBMD) hastalığının tespiti için bir BDT sistemi gerçekleştirilmiş ve patojenik drusen alanlarının büyüklüğünün hesaplanması sağlanmıştır. Çalışmanın amacı YBMD hastalığının görüldüğü alanların tespitinin ve büyüklüğünü hesaplamanın yanında hastalığa karşı uygulanan tedavinin sonucunun takibini de sağlamaktır. Geliştirilen sistemin yardımıyla optalmoloji uzmanları işaretlenen alanları kısa sürede tespit edebilecek ve hastalığın tedaviye verdiği cevabı basit bir şekilde gözlemleyebileceklerdir. Geliştirilen BDT sistemi 4 aşamadan oluşmaktadır, a) önişleme aşaması, b) bölütleme aşaması, c) ilgi alanı tespiti ve d) öznitelik çıkarma ve tespit aşaması. Geliştirilen BDT sistemi 75 görüntüden oluşan bir verisetiyle test edilmiştir. BST sisteminin elde ettiği sonuçlar bir optalmoloji uzmanıyla karşılaştırılarak sonuç bölümünde sunulmuştur. Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır