alexa Analysis of Vasculature Detection in Human Retinal Images Using Bacterial Foraging Optimization Based Multi Thresholding
ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
Open Access

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Research Article

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

Sri Madhava Raja N1*, Kavitha G2 and Ramakrishnan S3

1Department of Electronics and Instrumentation Engg., St. Joseph’s College of Engineering, OMR, Chennai, Tamil Nadu, India

2Department of Electronics Engineering, M.I.T. Campus, Anna University, Chennai, Tamil Nadu, India

3Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, Tamilnadu, India

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.

Abstract

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.

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