Pattern Recognition
Characterization and detection of noise in clustering
Pattern Recognition Letters
A fast thresholding selection procedure for multimodal and unimodal histograms
Pattern Recognition Letters
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Expert Systems with Applications: An International Journal
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
Application of a hybrid ant colony optimization for the multilevel thresholding in image processing
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
IEEE Transactions on Neural Networks
Bacterial foraging based moon symmetry axis estimation for spacecraft attitude determination
International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
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Segmentation of brain magnetic resonance images (MRIs) can be used to identify various neural disorders. The MRI segmentation facilitates in extracting different brain tissues such as white matter, gray matter and cerebrospinal fluids. Segmentation of these tissues helps in determining the volume of the tissues in three-dimensional brain MRI, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. In this article, multilevel thresholding based on adaptive bacterial foraging (ABF) algorithm is presented for brain MRI segmentation. The proposed ABF algorithm employs an adaptive step size to improve both exploration and exploitation capability of the BF algorithm. Maximization of the measure of separability on the basis of the entropy (Kapur) method and the between-class variance (Otsu) method, which are the two popular thresholding techniques, are employed to evaluate the performance of the proposed method. Application results to axial, T2-weighted brain MRI slices are provided to show the performance of the proposed segmentation approach. These results are compared with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) in terms of solution quality, robustness and computational efficiency.