Affiliations: [a]
Department of Electronics and Communication, Research Scholar, College of Engineering Trivandrum, Kerala | [b]
Department of Electronics and Communication, College of Engineering Trivandrum, Kerala | [c]
Department of Ophthalmology, Sree Gokulam Medical College and Research Foundation, Trivandrum, Kerala
Correspondence:
[*]
Corresponding author. R.S. Jeena, Department of Electronics and Communication, Research Scholar, College of Engineering Trivandrum, Kerala. E-mail: jeena_rs@yahoo.com.
Abstract: Stroke is a cerebrovascular disease which is one of the significant causes of adult impairment. Research shows that retinal fundus images carry vital information for the prediction of various cardiovascular diseases like Stroke. This work investigates a multi-texture description for the computer aided diagnosis of Stroke from retinal fundus images. Texture of the retinal background is analyzed, thereby eliminating the need for segmentation. Gabor Filter (GF), Local Binary Pattern (LBP) and Histogram of Oriented gradients (HOG) are the texture descriptors implemented in this work. The texture descriptors are applied to the second Eigen channel obtained by Principal Component Analysis (PCA). Extracted features are concatenated to form a multi-texture representation and dimensionality reduction is done by ReliefF feature selection method. The compact feature vector is given to Naïve Bayes classifier and performance metrics are evaluated. We have evaluated the performance of individual feature descriptors and multiple feature descriptors in retinal fundus images for stroke diagnosis. Multi-texture description outperforms individual texture descriptors by an accuracy of 95.1 %.
Keywords: Stroke, Gabor filter, local binary pattern, histogram of oriented gradients, principal component analysis, ReliefF
