Paper Contents
Abstract
This paper introduces a novel optimal multilevel thresholding algorithm for brain magnetic resonance image segmentation. The optimization algorithm, applied for image histogram-based thresholding, is based on a relatively recent evolutionary approach known as bacterial foraging. Originally proposed towards the end of the last millennium, bacterial foraging is emerging as a strong contender for distributed control and optimization. The utility of the proposed method is demonstrated by considering several benchmark brain MRI images. The performance of the proposed algorithm, henceforth called BFOR, is compared with another contemporary, popular artificial life-based approach introduced for solving complex stochastic optimization problems, namely particle swarm optimization with linearly varying inertia weight PSW. The results obtained for the benchmark images were quite encouraging as BFOR comprehensively outperformed PSW.
Copyright
Copyright © 2025 Blessy Queen Mary M. This is an open access article distributed under the Creative Commons Attribution License.
