Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images

Magnetic Resonance (MR) brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependant, difficult to reproduce and computationally expensive. To overcome these drawbacks, this paper proposes a new heuristic optimization algorithm, amended bacterial foraging (ABF) algorithm for multilevel thresholding of MR brain images. The optimal thresholds are found by maximizing Kapur’s (entropy criterion) and Otsu’s (between-class variance) thresholding functions using ABF algorithm. The proposed method is evaluated on 10 axial, T2 weighted MR brain image slices and compared with other evolutionary algorithms such as bacterial foraging (BF), particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From the experimental results, it is observed that the new method is computationally more efficient, prediction wise more accurate and shows faster convergence compared to BF, PSO and GA methods. Applying the proposed thresholding algorithm to these images can help for the best segmentation of gray matter, white matter and cerebrospinal fluid which offers the possibility of improved clinical decision making and diagnosis.

► Amended bacterial foraging algorithm for thresholding of MR brain images is proposed.
► The optimal thresholds are found by maximizing Kapur’s and Otsu’s functions.
► The proposed method is evaluated on 10 axial, T2 weighted MR brain image slices.
► It is found that the new method is computationally more efficient.