Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

Jamal GHASEMI; Mohammad Reza KARAMI MOLLAEI; Reza GHADERI; Ali HOJJATOLESLAMI

doi:10.1631/jzus.C1100288

Your Location：

Home >

Browse articles >

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

计算机与自动化 | Updated：2022-12-09

- Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
- Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
- 信息与电子工程前沿（英文） 2012年13卷第7期页码：520-533
- Affiliations：
  
  1.Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran
  2.School of Computing, University of Kent, Canterbury CT2 7PD, UK
- Author bio：
  
  [ "Jamal GHASEMI, E-mail: jghasemi@stu.nit.ac.ir" ]
- Funds：
- DOI：10.1631/jzus.C1100288
  中图分类号： TP391
- 收稿：2011-10-07，
  
  修回：2012-;5-;4，
  
  纸质出版：2012-07
- Accepted：
Scan QR Code
Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. 信息与电子工程前沿（英文）, 2012,13(7):520-533.

Jamal GHASEMI, Mohammad Reza KARAMI MOLLAEI, Reza GHADERI, et al. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(7): 520-533.
Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. 信息与电子工程前沿（英文）, 2012,13(7):520-533. DOI： 10.1631/jzus.C1100288.

Jamal GHASEMI, Mohammad Reza KARAMI MOLLAEI, Reza GHADERI, et al. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(7): 520-533. DOI： 10.1631/jzus.C1100288.

摘要

Abstract

As a result of noise and intensity non-uniformity

automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method

fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements. The results of the proposed method are evaluated using Dice similarity and Accuracy indices. Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.

关键词

Keywords

references

W. Abd-Almageed , , , A. El-Osery , , , C. Smith . . A fuzzy-statistical contour model for MRI segmentation and target tracking . . SPIE , , 2004 . . 5438 25 - - 33 . . DOI: 10.1117/12.541406 http://doi.org/10.1117/12.541406 . .

A. Afzalian , , , M.R. Karami Mollaei , , , M. Dousti , , , J. Ghasemi . . A new approach for speech enhancement based on singular value decomposition and wavelet transform . . Aust. J. Basic Appl. Sci. , , 2010 . . 4 ( ( 8 ): ): 3602 - - 3612 . . . .

M.N. Ahmed , , , S.M. Yamany , , , N. Mohamed , , , A.A. Farag , , , T. Moriarty . . A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data . . IEEE Trans. Med. Imag. , , 2002 . . 21 ( ( 3 ): ): 193 - - 199 . . DOI: 10.1109/42.996338 http://doi.org/10.1109/42.996338 . .

S.P. Awate , , , H. Zhang , , , T.J. Simon , , , J.C. Gee . . Multivariate Segmentation of Brain Tissues by Fusion of MRI and DTI Data . . Proc. 5th IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro , , 2008 . . p.213 - - 216 . . DOI: 10.1109/ISBI.2008.4540970 http://doi.org/10.1109/ISBI.2008.4540970 . .

M. Beynon , , , D. Cosker , , , D. Marshall . . An expert system for multi-criteria decision making using Dempster Shafer theory . . Expert Syst. Appl. , , 2001 . . 20 ( ( 4 ): ): 357 - - 367 . . DOI: 10.1016/S0957-4174(01)00020-3 http://doi.org/10.1016/S0957-4174(01)00020-3 . .

J.C. Bezdek . . Pattern Recognition with Fuzzy Objective Function Algorithms , , : : New York Plenum Press , , 1981 . . DOI: 10.1007/978-1-4757-0450-1 http://doi.org/10.1007/978-1-4757-0450-1 . .

E. Binaghi , , , P. Madella . . Fuzzy Dempster-Shafer reasoning for rule-based classifiers . . Int. J. Intell. Syst. , , 1999 . . 14 ( ( 6 ): ): 559 - - 583 . . DOI: 10.1002/(SICI)1098-111X(199906)14:6<559::AID-INT2>3.0.CO;2-# http://doi.org/10.1002/(SICI)1098-111X(199906)14:6<559::AID-INT2>3.0.CO;2-# . .

I. Bloch . . Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account . . Pattern Recogn. Lett. , , 1996 . . 17 ( ( 8 ): ): 905 - - 919 . . DOI: 10.1016/0167-8655(96)00039-6 http://doi.org/10.1016/0167-8655(96)00039-6 . .

M. Bomans , , , K.H. Hohne , , , U. Tiede , , , M. Riemer . . 3-D segmentation of MR images of the head for 3-D display . . IEEE Trans. Med. Imag. , , 1990 . . 9 ( ( 2 ): ): 177 - - 183 . . DOI: 10.1109/42.56342 http://doi.org/10.1109/42.56342 . .

M.E. Brandt , , , T.P. Bohan , , , L.A. Kramer , , , J.M. Fletcher . . Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images . . Comput. Med. Imag. Graph. , , 1994 . . 18 ( ( 1 ): ): 25 - - 34 . . DOI: 10.1016/0895-6111(94)90058-2 http://doi.org/10.1016/0895-6111(94)90058-2 . .

C. Brechbühler , , , G. Gerig , , , G. Székely . . Compensation of Spatial Inhomogeneity in MRI Based on a Multi-valued Image Model and a Parametric Bias Estimate . . Proc. Visualization in Biomedical Computing , , 1996 . . p.141 - - 146 . . DOI: 10.1007/BFb0046948 http://doi.org/10.1007/BFb0046948 . .

K.S. Chuang , , , H.L. Tzeng , , , S. Chen , , , J. Wu , , , T.J. Chen . . Fuzzy c-means clustering with spatial information for image segmentation . . Comput. Med. Imag. Graph. , , 2006 . . 30 ( ( 1 ): ): 9 - - 15 . . DOI: 10.1016/j.compmedimag.2005.10.001 http://doi.org/10.1016/j.compmedimag.2005.10.001 . .

A. Demirhan , , , I. Güler . . Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation . . Eng. Appl. Artif. Intell. , , 2011 . . 24 ( ( 2 ): ): 358 - - 367 . . DOI: 10.1016/j.engappai.2010.09.008 http://doi.org/10.1016/j.engappai.2010.09.008 . .

J. Ghasemi , , , M.R. Karami Mollaei . . A new approach for speech enhancement based on eigenvalue spectral subtraction . . Signal Process. Int. J. , , 2009 . . 3 ( ( 4 ): ): 34 - - 41 . . . .

J. Ghasemi , , , M.R. Karami Mollaei , , , R. Ghaderi , , , S.A. Hojjatoleslami . . Brain Tissue Segmentation by FCM and Dempster-Shafer Theory . . 7th Iranian Conf. on Machine Vision and Image Processing , , 2011 . . p.1 - - 5 . . DOI: 10.1109/IranianMVIP.2011.6121577 http://doi.org/10.1109/IranianMVIP.2011.6121577 . .

J.D. Gispert , , , S. Reig , , , J. Pascau , , , J.J. Vaquero , , , P. Garcia-Barreno , , , M. Desco . . Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error . . Human Brain Map. , , 2004 . . 22 ( ( 2 ): ): 133 - - 144 . . DOI: 10.1002/hbm.20013 http://doi.org/10.1002/hbm.20013 . .

A. Hadjiprocopis , , , W. Rashid , , , P.S. Tofts . . Unbiased segmentation of diffusion-weighted magnetic resonance images of the brain using iterative clustering . . Magn. Reson. Imag. , , 2005 . . 23 ( ( 8 ): ): 877 - - 885 . . DOI: 10.1016/j.mri.2005.07.010 http://doi.org/10.1016/j.mri.2005.07.010 . .

M. Hasanzadeh , , , S. Kasaei . . Multispectral Brain MRI Segmentation Based on Fuzzy Classifiers and Evidence Theory . . 15th Iranian Conf. on Electrical Engineering , , 2007 . . p.1 - - 5 . . . .

T. Heinonen , , , P. Dastidar , , , H. Eskola , , , H. Frey , , , P. Ryymin , , , E. Laasonen . . Applicability of semi-automatic segmentation for volumetric analysis of brain lesions . . J. Med. Eng. Technol. , , 1998 . . 22 ( ( 4 ): ): 173 - - 178 . . DOI: 10.3109/03091909809032536 http://doi.org/10.3109/03091909809032536 . .

L. Ji , , , H. Yan . . An attractable snakes based on the greedy algorithm for contour extraction . . Pattern Recogn. , , 2002 . . 35 ( ( 4 ): ): 791 - - 806 . . DOI: 10.1016/S0031-3203(01)00085-1 http://doi.org/10.1016/S0031-3203(01)00085-1 . .

Z.X. Ji , , , Q.S. Sun , , , D.S. Xia . . A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image . . Comput. Med. Imag. Graph. , , 2011 . . 35 ( ( 5 ): ): 383 - - 397 . . DOI: 10.1016/j.compmedimag.2010.12.001 http://doi.org/10.1016/j.compmedimag.2010.12.001 . .

A.W. Liew , , , H. Yan . . An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation . . IEEE Trans. Med. Imag. , , 2003 . . 22 ( ( 9 ): ): 1063 - - 1075 . . DOI: 10.1109/TMI.2003.816956 http://doi.org/10.1109/TMI.2003.816956 . .

A.W. Liew , , , H. Yan . . Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images . . Curr. Med. Imag. Rev. , , 2006 . . 2 ( ( 1 ): ): 91 - - 103 . . DOI: 10.2174/157340506775541604 http://doi.org/10.2174/157340506775541604 . .

T.C. Lin . . Switching-based filter based on Dempster's combination rule for image processing . . Inf. Sci. , , 2010 . . 180 ( ( 24 ): ): 4892 - - 4908 . . DOI: 10.1016/j.ins.2010.08.011 http://doi.org/10.1016/j.ins.2010.08.011 . .

T. McInerney , , , D. Terzopoulos . . Deformable models in medical image analysis: a survey . . Med. Image Anal. , , 1996 . . 1 ( ( 2 ): ): 91 - - 108 . . DOI: 10.1016/S1361-8415(96)80007-7 http://doi.org/10.1016/S1361-8415(96)80007-7 . .

W.J. Niessen , , , K.L. Vincken , , , J. Weickert , , , M.T.H. Romeny , , , M.A. Viergever . . Multiscale segmentation of three-dimensional MR brain images . . Int. J. Comput. Vis. , , 1999 . . 31 ( ( 2/3 ): ): 185 - - 202 . . DOI: 10.1023/A:1008070000018 http://doi.org/10.1023/A:1008070000018 . .

D.L. Pham , , , J.L. Prince . . An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities . . Pattern Recogn. Lett. , , 1999a . . 20 ( ( 1 ): ): 57 - - 68 . . DOI: 10.1016/S0167-8655(98)00121-4 http://doi.org/10.1016/S0167-8655(98)00121-4 . .

D.L. Pham , , , J.L. Prince . . Adaptive fuzzy segmentation of magnetic resonance images . . IEEE Trans. Med. Imag. , , 1999b . . 18 ( ( 9 ): ): 737 - - 752 . . DOI: 10.1109/42.802752 http://doi.org/10.1109/42.802752 . .

D.L. Pham , , , C. Xu , , , J.L. Prince . . A survey of current methods in medical image segmentation . . Ann. Rev. Biomed. Eng. , , 2000 . . 2 ( ( 1 ): ): 315 - - 337 . . DOI: 10.1146/annurev.bioeng.2.1.315 http://doi.org/10.1146/annurev.bioeng.2.1.315 . .

S. Prima , , , N. Ayache , , , T. Barrick , , , N. Roberts . . Maximum Likelihood Estimation of the Bias Field in MR Brain Images: Investigating Different Modelings of the Imaging Process . . Proc. 4th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention , , 2001 . . p.811 - - 819 . . . .

A. Rakar , , , D. Juricic , , , P. Ballé . . Transferable belief model in fault diagnosis . . Eng. Appl. Artif. Intell. , , 1999 . . 12 ( ( 5 ): ): 555 - - 567 . . DOI: 10.1016/S0952-1976(99)00030-5 http://doi.org/10.1016/S0952-1976(99)00030-5 . .

B. Scherrer , , , F. Forbes , , , C. Garbay , , , M. Dojat . . A joint Bayesian framework for MR brain scan tissue and structure segmentation based on distributed Markovian agents . . Comput. Intell. Healthcare 4 , , 2010 . . 309 81 - - 101 . . DOI: 10.1007/978-3-642-14464-6_5 http://doi.org/10.1007/978-3-642-14464-6_5 . .

G. Shafer . . A Mathematical Theory of Evidence , , : : Princeton Princeton University Press , , 1976 . . .

S. Shen , , , W. Sandham , , , M. Granat , , , A. Sterr . . MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization . . IEEE Trans. Inf. Technol. Biomed. , , 2005 . . 9 ( ( 3 ): ): 459 - - 467 . . DOI: 10.1109/TITB.2005.847500 http://doi.org/10.1109/TITB.2005.847500 . .

A. Simmons , , , P.S. Tofts , , , G.J. Barker , , , S.R. Arridge . . Sources of intensity nonuniformity in spin echo images at 1.5 T . . Magn. Reson. Med. , , 1994 . . 32 ( ( 1 ): ): 121 - - 128 . . DOI: 10.1002/mrm.1910320117 http://doi.org/10.1002/mrm.1910320117 . .

M.Y. Siyal , , , L. Yu . . An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI . . Pattern Recogn. Lett. , , 2005 . . 26 ( ( 13 ): ): 2052 - - 2062 . . DOI: 10.1016/j.patrec.2005.03.019 http://doi.org/10.1016/j.patrec.2005.03.019 . .

J.G. Sled , , , A.P. Zijdenbos , , , A.C. Evans . . A nonparametric method for automatic correction of intensity nonuniformity in MRI data . . IEEE Trans. Med. Imag. , , 1998 . . 17 ( ( 1 ): ): 87 - - 97 . . DOI: 10.1109/42.668698 http://doi.org/10.1109/42.668698 . .

M. Styner , , , C. Brechbuhler , , , G. Szekely , , , G. Gerig . . Parametric estimate of intensity inhomogeneities applied to MRI . . IEEE Trans. Med. Imag. , , 2000 . . 19 ( ( 3 ): ): 153 - - 165 . . DOI: 10.1109/42.845174 http://doi.org/10.1109/42.845174 . .

M. Tabassian , , , R. Ghaderi , , , R. Ebrahimpour . . Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence . . Expert Syst. Appl. , , 2011 . . 38 ( ( 5 ): ): 5259 - - 5267 . . DOI: 10.1016/j.eswa.2010.10.032 http://doi.org/10.1016/j.eswa.2010.10.032 . .

M. Tabassian , , , R. Ghaderi , , , R. Ebrahimpour . . Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels . . Expert Syst. Appl. , , 2012 . . 39 ( ( 2 ): ): 1698 - - 1707 . . DOI: 10.1016/j.eswa.2011.06.061 http://doi.org/10.1016/j.eswa.2011.06.061 . .

O. Tsang , , , A. Gholipour , , , N. Kehtarnavaz , , , I. Panahi , , , K. Gopinath , , , R. Briggs . . Comparison of Tissue Segmentation Algorithms in Neuroimage Analysis Software Tools . . Proc. 30th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society , , 2008 . . p.3924 - - 3928 . . DOI: 10.1109/IEMBS.2008.4650068 http://doi.org/10.1109/IEMBS.2008.4650068 . .

F. Valente . . Multi-stream speech recognition based on Dempster-Shafer combination rule . . Speech Commun. , , 2010 . . 52 ( ( 3 ): ): 213 - - 222 . . DOI: 10.1016/j.specom.2009.10.002 http://doi.org/10.1016/j.specom.2009.10.002 . .

J. Wang , , , J. Kong , , , Y. Lu , , , M. Qi , , , B. Zhang . . A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints . . Comput. Med. Imag. Graph. , , 2008 . . 32 ( ( 8 ): ): 685 - - 698 . . DOI: 10.1016/j.compmedimag.2008.08.004 http://doi.org/10.1016/j.compmedimag.2008.08.004 . .

R.R. Yager , , , J. Kacprzyk , , , M. Fedrizzi . . Advances in the Dempster-Shafer Theory of Evidence , , : : Chichester Wiley , , 1994 . . .

O.K. Yoon , , , D.M. Kwak , , , D.W. Kim , , , K.H. Park . . MR Brain Image Segmentation Using Fuzzy Clustering . . Proc. IEEE Int. Fuzzy Systems Conf. , , 1999 . . 2 853 - - 857 . . DOI: 10.1109/FUZZY.1999.793060 http://doi.org/10.1109/FUZZY.1999.793060 . .

D.Q. Zhang , , , S.C. Chen . . A novel kernelized fuzzy c-means algorithm with application in medical image segmentation . . Artif. Intell. Med. , , 2004 . . 32 ( ( 1 ): ): 37 - - 50 . . DOI: 10.1016/j.artmed.2004.01.012 http://doi.org/10.1016/j.artmed.2004.01.012 . .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据