群知能と進化的計算の国際ジャーナル

概要

Analysis of Vasculature Detection in Human Retinal Images Using Bacterial Foraging Optimization Based Multi Thresholding

 N Sri Madhava Raja*, G Kavitha and S Ramakrishnan

Analysis of blood vessels in digital retinal fundus images is an important problem attempted in contemporary

biomedical engineering research. In this work, normal and abnormal retinal images are pre-processed with adaptive histogram equalization and fuzzy filtering. Pre-processed images are then subjected to Tsallis multi-level thresholding method. The threshold levels determined by the chosen method are further optimized using bacterial foraging optimization techniques in order to improve the vessel content. The obtained results are validated using similarity measures by comparing with the corresponding ground truth of each image. Statistical and Tamura features are derived from optimal multi-level thresholding output images to analyse the healthy and pathological images. Results demonstrate that attempted series of pre-processing techniques enhances the edge information considerably and improves the efficacy of segmentation. It is observed that bacterial foraging optimization for Tsallis multi-level thresholding is able to extract retinal vasculature. Similarity measures show that this method provides considerable improvement in the extraction of vessel edges. Further, the statistical and Tamura features derived from detected vessels provide better differentiation between healthy and pathological images. As presence and absence of vessels in retina are clinically significant, the findings seem to be useful.