Abstract
Brain magnetic resonance (MR) image segmentation is pivotal for quantitative brain analyses, in which statistical models are most commonly used. However, in spite of its computational effectiveness, these models are less capable of handling the intensity non-uniformity (INU) and partial volume effect (PVE), and hence may produce less accurate results. In this paper, a novel brain MR image segmentation algorithm is proposed. To address the INU and PVE, voxel values in each small volume are characterized by a local variational Bayes (LVB) model, which is inferred by the niche differential evolution (NDE) technique to avoid local optima. A probabilistic brain atlas is constructed for each image to incorporate the anatomical prior into the segmentation process. The proposed NDE-LVB algorithm has been compared to the variational expectation-maximization based and genetic algorithm based segmentation algorithms and the segmentation routine in the widely used statistical parametric mapping package on both synthetic and clinical brain MR images. Our results suggest that the NDE-LVB algorithm can differentiate major brain tissue types more effectively and produce more accurate segmentation results.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61401209 & 61471297, in part by the Natural Science Foundation Youth Project of Jiangsu Province, China, under Grant BK20140790, in part by China Postdoctoral Science Foundation under Grants 2014T70525 & 2013M531364, in part by the Fundamental Research Funds for the Central Universities under Grants 3102014JSJ0006, and in part by the Returned Overseas Scholar Project of Shaanxi Province, China.
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Li, Z., Ji, Z., Xia, Y. (2015). Local Variational Bayesian Inference Using Niche Differential Evolution for Brain Magnetic Resonance Image Segmentation. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_60
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DOI: https://doi.org/10.1007/978-3-319-23989-7_60
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