Analysis of Vasculature in Human Retinal Images Using Particle Swarm Optimization Based Tsallis Multi-level Thresholding and Similarity Measures

Retinal vasculature of the human circulatory system which can be visualized directly provides a number of systemic conditions and can be diagnosed by the detection of lesions. Changes in these structures are found to be correlated with pathological conditions and provide information on severity or state of various diseases. In this work, particle swarm optimization algorithm based multilevel thresholding is adopted for detecting the vasculature structures in retinal fundus images. Initially, adaptive histogram equalization is used for pre-processing of the original images. Tsallis multilevel thresholding is used for the segmentation of the blood vessels. Further, similarity measures are used to quantify the similarity between the segmented result and the corresponding ground truth. The optimal multi-threshold selection using particle swarm optimization seems to provide better results. Similarity measures analysis using dendrogram and box plot provide validation of the segmentation procedure attempted.

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