A SEQUENTIAL LEARNING METHOD FOR DETECTION AND CLASSIFICATION OF EXUDATES IN RETINAL IMAGES TO ASSESS DIABETIC RETINOPATHY

One of the greatest concerns to the personnel in the current health care sector is the severe progression of diabetes. People can often have diabetes and be completely unaware as the symptoms seem harmless when they are seen on their own. Diabetic retinopathy (DR) is an eye disease that is associated with long-standing diabetes. Retinopathy can occur with all types of diabetes and can lead to blindness if left untreated. The conventional method followed by ophthalmologists is the regular testing of the retina. As this method takes time and energy of the ophthalmologists, a new feature-based automated technique for classification and detection of exudates in color fundus image is proposed in this paper. This method reduces the work of the professionals while examining every fundus image rather than only on abnormal image. The exudates are detected from the color fundus image by applying a few pre-processing techniques that remove the optic disk and similar blood vessels using morphological operations. The pre-processed image was then applied for feature extraction and these features were utilized for classification purpose. In this paper, a novel classification technique such as self-adaptive resource allocation network (SRAN) and meta-cognitive neural network (McNN) classifier is employed for classification of images as exudates, their severity and nonexudates. SRAN classifier makes use of self-adaptive thresholds to choose the appropriate training samples and removes the redundant samples to prevent over-training. These selected samples are availed to improve the classification performance. McNN classifier employs human-like meta-cognition to regulate the sequential learning process. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. It is therefore evident that the implementation of human meta-cognitive learning principle improves efficient learning.

Automated detection of exudates and macula for grading of diabetic macular edema

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.

Automated detection and grading of diabetic maculopathy in digital retinal images

Diabetic maculopathy is one of the retinal abnormalities in which a diabetic patient suffers from severe vision loss due to the affected macula. It affects the central vision of the person and causes blindness in severe cases. In this article, we propose an automated medical system for the grading of diabetic maculopathy that will assist the ophthalmologists in early detection of the disease. The proposed system extracts the macula from digital retinal image using the vascular structure and optic disc location. It creates a binary map for possible exudate regions using filter banks and formulates a detailed feature vector for all regions. The system uses a Gaussian Mixture Model-based classifier to the retinal image in different stages of maculopathy by using the macula coordinates and exudate feature set. The evaluation of proposed system is performed by using publicly available standard retinal image databases. The results of our system have been compared with other methods in the literature in terms of sensitivity, specificity, positive predictive value and accuracy. Our system gives higher values as compared to others on the same databases which makes it suitable for an automated medical system for grading of diabetic maculopathy.

Computer-aided diagnosis of diabetic retinopathy: a review

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.

Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy

Medical image analysis is a very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness. An automated system for early detection of DR can save a patient's vision and can also help the ophthalmologists in screening of DR. The background or nonproliferative DR contains four types of lesions, i.e., microaneurysms, hemorrhages, hard exudates, and soft exudates. This paper presents a method for detection and classification of exudates in colored retinal images. We present a novel technique that uses filter banks to extract the candidate regions for possible exudates. It eliminates the spurious exudate regions by removing the optic disc region. Then it applies a Bayesian classifier as a combination of Gaussian functions to detect exudate and nonexudate regions. The proposed system is evaluated and tested on publicly available retinal image databases using performance parameters such as sensitivity, specificity, and accuracy. We further compare our system with already proposed and published methods to show the validity of the proposed system.

Development of a mobile phone based ophthalmoscope for telemedicine

Regular retinal examinations can contribute to the management of both hypertensive and diabetic retinopathy. One of the most successful means of evaluating these retinopathies is by means of a fundus camera generating a fundus photograph. Patients in rural clinics unfortunately often lack the financial means to undergo regular examinations. This study produced a handheld ophthalmoscope that combines with a digital camera to capture retinal images. The images are transferred to a mobile phone and then sent to a central website for evaluation. The evaluation report is automatically returned to the mobile phone via SMS. The quality of the images was rated as acceptable for clinical use by medical specialists at the Department of Ophthalmology of the Health Sciences Faculty of Stellenbosch University, South Africa.

Automatic assessment of macular edema from color retinal images

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Assessment of four neural network based classifiers to automatically detect red lesions in retinal images

Diabetic retinopathy (DR) is an important cause of visual impairment in industrialised countries. Automatic detection of DR early markers can contribute to the diagnosis and screening of the disease. The aim of this study was to automatically detect one of such early signs: red lesions (RLs), like haemorrhages and microaneurysms. To achieve this goal, we extracted a set of colour and shape features from image regions and performed feature selection using logistic regression. Four neural network (NN) based classifiers were subsequently used to obtain the final segmentation of RLs: multilayer perceptron (MLP), radial basis function (RBF), support vector machine (SVM) and a combination of these three NNs using a majority voting (MV) schema. Our database was composed of 115 images. It was divided into a training set of 50 images (with RLs) and a test set of 65 images (40 with RLs and 25 without RLs). Attending to performance and complexity criteria, the best results were obtained for RBF. Using a lesion-based criterion, a mean sensitivity of 86.01% and a mean positive predictive value of 51.99% were obtained. With an image-based criterion, a mean sensitivity of 100%, mean specificity of 56.00% and mean accuracy of 83.08% were achieved.