Masters Thesis

Fusion of Reduced Color Texture Descriptors for Diabetic Retinopathy Recognition.

Diabetic retinopathy (DR) is a common eye disease that could lead to irreversible vision loss but hard to be noticed by carriers in early stages. On the path of recognizing DR stages by multi-scale color uniform local binary pattern in retinopathy images, this work explores two main point. The first point is investigating the role of feature dimensionality reduction in the process of extracting discriminatory features for effective classification. The second point is exploring the discriminatory information carried in different color spaces for fundus images. Experiments are conducted on a large scale dataset of 35,126 training images and 53,576 testing images that have been taken by different devices with high variance in dimension, quality and luminance. The proposed multi-level feature dimensionality reduction (FDR) methodology is applied in three scopes in the feature hierarchy of the fusion of five color spaces: RGB, L*a*b*, HSI, I1I2I3, and rgb. The novel combination of the proposed multi-level FDR method and color fusion achieves 75.2% accuracy by one-to-one SVM classifier.

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