A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
Pattern Recognition Letters
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
Journal of Medical Systems
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Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.