Analysis of Vasculature Detection in Human Retinal Images Using Bacterial Foraging Optimization Based Multi Thresholding
- Corresponding Author:
- Sri Madhava Raja N
Department of Electronics and Instrumentation Engg.
St. Joseph’s College of Engineering
Chennai, Tamil Nadu, India
Tel: 044 2257 5052
E-mail: [email protected]
Received date: October 16, 2013; Accepted date: December 30, 2013; Published date: January 15, 2014
Citation: Sri Madhava Raja N, Kavitha G, Ramakrishnan S (2014) Analysis of Vasculature Detection in Human Retinal Images Using Bacterial Foraging Optimization Based Multi Thresholding. Int J Swarm Intel Evol Comput 4:107. doi:
Copyright: © 2014 Sri Madhava Raja N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Analysis of blood vessels in digital retinal fundus images is an important problem attempted in contemporarybiomedical 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.