Full Text:   <5317>

CLC number: TP391

On-line Access: 2012-07-06

Received: 2011-10-07

Revision Accepted: 2012-04-13

Crosschecked: 2012-05-04

Cited: 6

Clicked: 9507

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.7 P.520-533


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

Author(s):  Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami

Affiliation(s):  Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran; more

Corresponding email(s):   jghasemi@stu.nit.ac.ir

Key Words:  Magnetic resonance imaging (MRI), Segmentation, Fuzzy c-mean (FCM), Dempster-Shafer theory (DST)

Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Journal of Zhejiang University Science C, 2012, 13(7): 520-533.

@article{title="Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory",
author="Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
%A Jamal Ghasemi
%A Mohammad Reza Karami Mollaei
%A Reza Ghaderi
%A Ali Hojjatoleslami
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 7
%P 520-533
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100288

T1 - Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
A1 - Jamal Ghasemi
A1 - Mohammad Reza Karami Mollaei
A1 - Reza Ghaderi
A1 - Ali Hojjatoleslami
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 7
SP - 520
EP - 533
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100288

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abd-Almageed, W., El-Osery, A., Smith, C., 2004. A fuzzy-statistical contour model for MRI segmentation and target tracking. SPIE, 5438:25-33.

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

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

[4]Awate, S.P., Zhang, H., Simon, T.J., Gee, J.C., 2008. Multivariate Segmentation of Brain Tissues by Fusion of MRI and DTI Data. Proc. 5th IEEE Int. Symp. on Biomedical Imaging: from Nano to Macro, p.213-216.

[5]Beynon, M., Cosker, D., Marshall, D., 2001. An expert system for multi-criteria decision making using Dempster Shafer theory. Expert Syst. Appl., 20(4):357-367.

[6]Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.

[7]Binaghi, E., Madella, P., 1999. Fuzzy Dempster-Shafer reasoning for rule-based classifiers. Int. J. Intell. Syst., 14(6):559-583.

[8]Bloch, I., 1996. Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn. Lett., 17(8):905-919.

[9]Bomans, M., Hohne, K.H., Tiede, U., Riemer, M., 1990. 3-D segmentation of MR images of the head for 3-D display. IEEE Trans. Med. Imag., 9(2):177-183.

[10]Brandt, M.E., Bohan, T.P., Kramer, L.A., Fletcher, J.M., 1994. Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Comput. Med. Imag. Graph., 18(1):25-34.

[11]Brechbühler, C., Gerig, G., Székely, G., 1996. Compensation of Spatial Inhomogeneity in MRI Based on a Multi-valued Image Model and a Parametric Bias Estimate. Proc. Visualization in Biomedical Computing, p.141-146.

[12]Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J., 2006. Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imag. Graph., 30(1):9-15.

[13]Demirhan, A., Güler, I., 2011. Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng. Appl. Artif. Intell., 24(2):358-367.

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

[15]Ghasemi, J., Karami Mollaei, M.R., Ghaderi, R., Hojjatoleslami, S.A., 2011. Brain Tissue Segmentation by FCM and Dempster-Shafer Theory. 7th Iranian Conf. on Machine Vision and Image Processing, p.1-5.

[16]Gispert, J.D., Reig, S., Pascau, J., Vaquero, J.J., Garcia-Barreno, P., Desco, M., 2004. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Human Brain Map., 22(2):133-144.

[17]Hadjiprocopis, A., Rashid, W., Tofts, P.S., 2005. Unbiased segmentation of diffusion-weighted magnetic resonance images of the brain using iterative clustering. Magn. Reson. Imag., 23(8):877-885.

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

[19]Heinonen, T., Dastidar, P., Eskola, H., Frey, H., Ryymin, P., Laasonen, E., 1998. Applicability of semi-automatic segmentation for volumetric analysis of brain lesions. J. Med. Eng. Technol., 22(4):173-178.

[20]Ji, L., Yan, H., 2002. An attractable snakes based on the greedy algorithm for contour extraction. Pattern Recogn., 35(4):791-806.

[21]Ji, Z.X., Sun, Q.S., Xia, D.S., 2011. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput. Med. Imag. Graph., 35(5):383-397.

[22]Liew, A.W., Yan, H., 2003. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag., 22(9):1063-1075.

[23]Liew, A.W., Yan, H., 2006. Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr. Med. Imag. Rev., 2(1):91-103.

[24]Lin, T.C., 2010. Switching-based filter based on Dempster’s combination rule for image processing. Inf. Sci., 180(24):4892-4908.

[25]McInerney, T., Terzopoulos, D., 1996. Deformable models in medical image analysis: a survey. Med. Image Anal., 1(2):91-108.

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

[27]Pham, D.L., Prince, J.L., 1999a. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recogn. Lett., 20(1):57-68.

[28]Pham, D.L., Prince, J.L., 1999b. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag., 18(9):737-752.

[29]Pham, D.L., Xu, C., Prince, J.L., 2000. A survey of current methods in medical image segmentation. Ann. Rev. Biomed. Eng., 2(1):315-337.

[30]Prima, S., Ayache, N., Barrick, T., Roberts, N., 2001. 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, p.811-819.

[31]Rakar, A., Juricic, D., Ballé, P., 1999. Transferable belief model in fault diagnosis. Eng. Appl. Artif. Intell., 12(5):555-567.

[32]Scherrer, B., Forbes, F., Garbay, C., Dojat, M., 2010. A joint Bayesian framework for MR brain scan tissue and structure segmentation based on distributed Markovian agents. Comput. Intell. Healthcare 4, 309:81-101.

[33]Shafer, G., 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton.

[34]Shen, S., Sandham, W., Granat, M., Sterr, A., 2005. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inf. Technol. Biomed., 9(3):459-467.

[35]Simmons, A., Tofts, P.S., Barker, G.J., Arridge, S.R., 1994. Sources of intensity nonuniformity in spin echo images at 1.5 T. Magn. Reson. Med., 32(1):121-128.

[36]Siyal, M.Y., Yu, L., 2005. An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI. Pattern Recogn. Lett., 26(13):2052-2062.

[37]Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag., 17(1):87-97.

[38]Styner, M., Brechbuhler, C., Szekely, G., Gerig, G., 2000. Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. Med. Imag., 19(3):153-165.

[39]Tabassian, M., Ghaderi, R., Ebrahimpour, R., 2011. Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence. Expert Syst. Appl., 38(5):5259-5267.

[40]Tabassian, M., Ghaderi, R., Ebrahimpour, R., 2012. Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels. Expert Syst. Appl., 39(2):1698-1707.

[41]Tsang, O., Gholipour, A., Kehtarnavaz, N., Panahi, I., Gopinath, K., Briggs, R., 2008. Comparison of Tissue Segmentation Algorithms in Neuroimage Analysis Software Tools. Proc. 30th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, p.3924-3928.

[42]Valente, F., 2010. Multi-stream speech recognition based on Dempster-Shafer combination rule. Speech Commun., 52(3):213-222.

[43]Wang, J., Kong, J., Lu, Y., Qi, M., Zhang, B., 2008. A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput. Med. Imag. Graph., 32(8):685-698.

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

[45]Yoon, O.K., Kwak, D.M., Kim, D.W., Park, K.H., 1999. MR Brain Image Segmentation Using Fuzzy Clustering. Proc. IEEE Int. Fuzzy Systems Conf., 2:853-857.

[46]Zhang, D.Q., Chen, S.C., 2004. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med., 32(1):37-50.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE