TY - GEN
T1 - Local variational bayesian inference using niche differential evolution for brain magnetic resonance image segmentation
AU - Li, Zhe
AU - Ji, Zexuan
AU - Xia, Yong
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Gaussian mixture model
KW - Image segmentation
KW - Magnetic resonance imaging (MRI)
KW - Niche differential evolution
KW - Variational bayes inference
UR - http://www.scopus.com/inward/record.url?scp=84951734821&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23989-7_60
DO - 10.1007/978-3-319-23989-7_60
M3 - 会议稿件
AN - SCOPUS:84951734821
SN - 9783319239873
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 592
EP - 602
BT - Intelligence Science and Big Data Engineering
A2 - He, Xiaofei
A2 - Zhou, Zhi-Hua
A2 - Gao, Xinbo
A2 - Liu, Zhi-Yong
A2 - Zhang, Yanning
A2 - Fu, Baochuan
A2 - Hu, Fuyuan
A2 - Zhang, Zhancheng
PB - Springer Verlag
T2 - 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Y2 - 14 June 2015 through 16 June 2015
ER -