Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22729
Title: Hybrid genetic and variational expectation-maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation
Authors: Tian, G
Xia, Y
Zhang, Y
Feng, D
Keywords: modeling.
Issue Date: 2011
Source: IEEE Transactions on information technology in biomedicine, 2011, v. 15, no. 3, p. 373-380 How to cite?
Journal: IEEE Transactions on information technology in biomedicine 
Abstract: The expectation-maximization (EM) algorithm has been widely applied to the estimation of Gaussian mixture model (GMM) in brain MR image segmentation. However, the EM algorithm is deterministic and intrinsically prone to overfitting the training data and being trapped in local optima. In this paper, we propose a hybrid genetic and variational EM (GA-VEM) algorithm for brain MR image segmentation. In this approach, the VEM algorithm is performed to estimate the GMM, and the GA is employed to initialize the hyperparameters of the conjugate prior distributions of GMM parameters involved in the VEM algorithm. Since GA has the potential to achieve global optimization and VEM can steadily avoid overfitting, the hybrid GA-VEM algorithm is capable of overcoming the drawbacks of traditional EM-based methods. We compared our approach to the EM-based, VEM-based, and GA-EM based segmentation algorithms, and the segmentation routines used in the statistical parametric mapping package and FMRIB Software Library in 20 low-resolution and 17 high-resolution brain MR studies. Our results show that the proposed approach can improve substantially the performance of brain MR image segmentation.
URI: http://hdl.handle.net/10397/22729
ISSN: 1089-7771
DOI: 10.1109/TITB.2011.2106135
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

36
Last Week
0
Last month
1
Citations as of Apr 30, 2016

WEB OF SCIENCETM
Citations

32
Last Week
0
Last month
1
Citations as of Jan 12, 2017

Page view(s)

17
Last Week
0
Last month
Checked on Jan 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.