期刊
NEUROCOMPUTING
卷 74, 期 14-15, 页码 2299-2313出版社
ELSEVIER
DOI: 10.1016/j.neucom.2011.03.010
关键词
Segmentation; MRI; Multilevel thresholding; Adaptive bacterial foraging; Kapur; Otsu
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. (C) 2011 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据