Machine Learning
Digital Image Processing
Top-Down and Bottom-Up Strategies in Lesion Detection of Background Diabetic Retinopathy
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
Pattern Recognition Letters
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Journal of Medical Systems
Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages
Journal of Medical Systems
A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Retinal images: optic disk localization and detection
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding
Journal of Medical Systems
Journal of Medical Systems
Automatic fovea location in retinal images using anatomical priors and vessel density
Pattern Recognition Letters
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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.