JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (3): 49-55.doi: 10.6040/j.issn.1672-3961.0.2016.310

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Brain MR image segmentation based on student's t mixture model with Markov random field

LI Lu, FAN Wentao, DU Jixiang*   

  1. College of Computer Science, Huaqiao University, Xiamen 361021, Fujian, China
  • Received:2016-07-22 Online:2017-06-20 Published:2016-07-22

Abstract: Extra computation was always needed when using Expectation Maximization(EM)algorithm for solving mixture models. To overcome this drawback, a novel Student's t-mixture model based on Markov random field was proposed. EM algorithm was used directly in the proposed model, which was convenient and efficient. According to the experimental results, the proposed method could overcome the impact of noise on the segmentation results efficiently, and got better segmentation results.

Key words: brain MR image segmentation, Markov random field, expectation maximization algorithm, student's t-mixture model

CLC Number: 

  • TP391
[1] 江贵平, 秦文健, 周寿军,等. 医学图像分割及其发展现状[J].计算机学报, 2015, 38(6):1222-1242. JIANG Guiping, QIN Wenjia, ZHOU Shoujun, et al. State-of-the-art in medical image segmentation [J]. Chinese Journal of Computers, 2015, 38(6):1222-1242.
[2] ZHANG H, WEN T, ZHENG Y, et al. Two fast and robust modified Gaussian mixture models incorporating local spatial information for image segmentation [J].Journal of Signal Processing Systems, 2015, 81(1):45-58.
[3] NGUYEN T M, WU Q M J. Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem[J].IEEE Transactions on Systems,Man and Cybernetics, Part B(Cybernetics), 2012, 42(1):193-202.
[4] ZHANG Y, BRADY M, SMITH S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J].IEEE Transactions on Medical Imaging, 2001, 20(1):45-57.
[5] JI Z, XIA Y, SUN Q, et al. Adaptive scale fuzzy local Gaussian mixture model for brain MR image segmentation[J].Neurocomputing, 2014, 134(6):60-69.
[6] JI J, WANG K L. A fuzzy clustering algorithm with robust spatially constraint for brain MR image segmentation[C] //Proceedings of the 2014 IEEE International Conference on Fuzzy Systems(FUZZ-IEEE).Beijing, China:IEEE, 2014:202-209.
[7] JI Z, LIU J, CAO G, et al. Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation[J].Pattern Recognition, 2014, 47(7):2454-2466.
[8] SHAO G, GAO J, WANG T, et al. Fuzzy c-means clustering with a new regularization term for image segmentation[C] //Proceedings of the 2014 International Joint Conference on Neural Networks(IJCNN).Beijing, China:IEEE, 2014: 2862-2869.
[9] DONG F, PENG J. Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization[J].Journal of Visual Communication & Image Representation, 2014, 25(5):827-839.
[10] GREENSPAN H, RUF A, GOLDBERGER J. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images[J]. IEEE Transactions on Medical Imaging, 2006, 25(9):1233-1245.
[11] SONG Y, JI Z, SUN Q. An extension Gaussian mixture model for brain MRI segmentation[J]. Conference: International Conference of the IEEE Engineering in Medicine & Biology Society IEEE Engineering in Medicine & Biology Society Conference. Conf Proc IEEE Eng Med Biol Soc, 2014:4711-4714.
[12] BALAFAR M A, RAMLI A R, SARIPAN M I, et al. Review of brain MRI image segmentation methods[J]. Artificial Intelligence Review, 2010, 33(3):261-274.
[13] SKIBBE H, REISERT M, BURKHARDT H. Gaussian neighborhood descriptors for brain segmentation [C] //Proceedings of the 12th IAPR Conference on Machine Vision Applications(MVA 2011). Nara, Japan: IAPR, 2011:35-38.
[14] SFIKAS G, NIKOU C, GALATSANOS N. Robust image segmentation with mixtures of student's t-distributions[C] //Proceedings of the 2007 IEEE International Conference on Image Processing. San Antonio, USA:IEEE, 2007: I-273-I-276.
[15] SFIKAS G, NIKOU C, GALATSANOS N, et al. MR brain tissue classification using an edge-preserving spatially variant Bayesian mixture model[J].Medical Image Computing and Computer-assisted Intervention-MICCAI 2008, 2008, 11(1):43-50.
[16] SFIKAS G, NIKOU C, GALATSANOS N, et al. Spatially varying mixtures incorporating line processes for image segmentation[J].Journal of Mathematical Imaging and Vision, 2010, 36(2):91-110.
[17] DIPLAROS A, VLASSIS N, GEVERS T. A spatially constrained generative model and an EM algorithm for image segmentation[J].IEEE Transactions on Neural Networks, 2007, 18(3):798-808.
[18] NIKOU C, GALATSANOS N P, LIKAS A C. A class-adaptive spatially variant mixture model for image segmentation[J].IEEE Transactions on Image Processing, 2007, 16(4):1121-1130.
[19] BLEKAS K, LIKAS A, GALATSANOS N P, et al. A spatially constrained mixture model for image segmentation[J].IEEE Transactions on Neural Networks, 2005, 16(2):494-498.
[20] NGUYEN T M, WU Q M J. Robust student's-t mixture model with spatial constraints and its application in medical image segmentation[J].IEEE Transactions on Medical Imaging, 2012, 31(1):103-116.
[21] NIKOU C, LIKAS A C, GALATSANOS N P. A Bayesian framework for image segmentation with spatially varying mixtures[J].IEEE Transactions on Image Processing, 2010, 19(9):2278-2289.
[22] ASHBURNER J, FRISTON K J. Unified segmentation[J].Neuroimage, 2005, 26(3):839-851.
[23] PEEL D, MCLACHLAN G J. Robust mixture modeling using the t distribution[J].Statistics & Computing, 2000, 10(4):339-348.
[24] NGUYEN T M, WU Q M J. Fast and robust spatially constrained Gaussian mixture model for image segmentation[J].IEEE Transactions on Circuits & Systems for Video Technology, 2013, 23(4):621-635.
[25] CHATZIS S P, VARVARIGOU T A. A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation[J].IEEE Transactions on Fuzzy Systems, 2008, 16(5):1351-1361.
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