Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Osareh, A., Shadgar, B., Markham, R. (2005). Comparative Pixel-Level Exudate Recognition in Colour Retinal Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_109
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DOI: https://doi.org/10.1007/11559573_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
Online ISBN: 978-3-540-31938-2
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