Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Classification and Localisation of Diabetic-Related Eye Disease
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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
A morphologic three-stage approach for detecting exudates in color eye fundus images
Proceedings of the 2010 ACM Symposium on Applied Computing
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Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a visible sign of retinal diseases such as diabetic retinopathy. Following some key preprocessing steps, colour retinal image pixels are classified to exudate and non-exudate classes. K nearest neighbour, Gaussian quadratic and Gaussian mixture model classifiers are investigated within the pixel-level exudate recognition framework. A Gaussian mixture model-based classifier demonstrated the best classification performance with 89.2% sensitivity and 81.0% predictivity in terms of pixel-level accuracy and 92.5% sensitivity and 81.4% specificity in terms of image-based accuracy.