Detection and diabetic retinopathy grading using digital retinal images

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease

Diabetic retinopathy detection with texture features

Diabetic retinopathy is one of the leading causes of visual impairment and blindness in the world and the prevalence keeps increasing. It is a vascular disorder of the retina and a symptom of diabetes mellitus. The health of the retina is studied with non-invasive retinal imaging. However, the analysis of the retinal images is laborious, subjective and the number of images to be reviewed is increasing. In this master’s thesis, a computer-aided detection system for diabetic retinopathy, microaneurysms and small hemorrhages was designed and implemented. The purpose of this study was to find out, are texture features able to produce descriptive and efficient information for the retinal image classification and is the implemented system accurate. The process included image preprocessing, extraction of 21 texture features, feature selection and classification with a support vector machine. The retinal image datasets that were used for the testing were Messidor, DIARETDB1 and e-ophta. The texture features were not successful when classifying the retinal images into diabetic retinopathy or normal. The best average accuracy was 69 % with 72 % average sensitivity and 66 % average specificity. The texture features are not that descriptive as global features with a whole retinal image. Additionally, the varying size of the images and variation within a class affected the classification. The second experiment studied the classification of images into microaneurysm or normal by dividing the retinal images into blocks. The texture features were successful when the images were divided into small blocks of size 50*50. The best average accuracy was 96 % with 96 % average sensitivity and 96 % average specificity. The texture features are more descriptive in the local setting since then they can extract finer details. To ease the clinical workflow of ophthalmologists and other experts, the computer-aided detection system can lower the manual labor and make retinal image analysis more efficient, accurate and precise. To develop the systems further, an optic disc and image quality detectors are needed