[1] W. Abd-Almageed, A. El-Osery and C. Smith, A fuzzy-statistical contour model for MRI
segmentation and target tracking, presented at the SPIE, Orlando, FL, USA, (2004), 25{33.
[2] M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag and T. Moriarty, A modi ed fuzzy c-
means algorithm for bias eld estimation and segmentation of MRI data, IEEE transactions
on medical imaging, 21(3) (2002), 193{199.
[3] S. P. Awate, H. Zhang, T. J. Simon and J. C. Gee, Multivariate segmentation of brain tissues
by fusion of MRI and DTI data, presented at the Proceedings of the 2008 IEEE International
Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, (2008).
[4] M. Balafar, A. Ramli, M. Saripan and S. Mashohor, Review of brain MRI image segmentation
methods, Arti cial Intelligence Review, 33(3) (2010), 261{274.
[5] M. Beynon, D. Cosker and D. Marshall, An expert system for multi-criteria decision making
using Dempster Shafer theory, Expert Systems with Applications, 20(4) (2001), 357{367.
[6] E. Binaghi and P. Madella, Fuzzy DempsterShafer reasoning for rule-based classi ers, Inter-
national Journal of Intelligent Systems, 14(6) (1999), 559-583.
[7] I. Bloch, Some aspects of Dempster-Shafer evidence theory for classi cation of multi-modality
medical images taking partial volume e ect into account, Pattern Recognition Letters, 17(8)
(1996), 905{919.
[8] M. Bomans, K. H. Hohne, U. Tiede and M. Riemer, 3-D segmentation of MR images of the
head for 3-D display, IEEE transactions on medical imaging, 9(2) (1990), 177{183.
[9] C. Brechbhler, G. Gerig and G. Szkely, Compensation of spatial inhomogeneity in MRI based
on a multi-valued image model and a parametric bias estimate, In Visualization in Biomedical
Computing, (1996), 141{146.
[10] K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu and T. J. Chen, Fuzzy c-means clustering with
spatial information for image segmentation, Computerized Medical Imaging and Graphics :
the Ocial Journal of the Computerized Medical Imaging Society, 30(1) (2006), 9-151.
[11] A. Demirhan and I. Gler, Combining stationary wavelet transform and self-organizing maps
for brain MR image segmentation, Engineering Applications of Arti cial Intelligence, 24(2)
(2011), 358{367.
[12] J. Ghasemi, R. Ghaderi, M. R. Karami Mollaei and A. Hojjatoleslami, Separation of brain tis-
sues in MRI based on multi-dimensional FCM and spatial information, Eighth International
Conference on in Fuzzy Systems and Knowledge Discovery (FSKD), (2011), 247{251.
[13] J. Ghasemi, M. R. Karami Mollaei, R. Ghaderi and A. Hojjatoleslami, Brain tissue segmen-
tation based on spatial information fusion by Dempster-Shafer theory, Journal of Zhejiang
University - Science C, 13(7) (2012), 520{533.
[14] J. D. Gispert, S. Reig, J. Pascau, J. J. Vaquero, P. Garcia-Barreno and M. Desco, Method for
bias eld correction of brain T1-weighted magnetic resonance images minimizing segmenta-
tion error, Human brain mapping, 22(2) (2004), 133{144.
[15] M. Hasanzadeh and S. Kasaei, Multispectral Brain MRI Segmentation based on Fuzzy Clas-
si ers and Evidence Theory, presented at the 15th Iranian Conference on Electrical Engi-
neering, ICEE, Tehran, Iran, 2007.
[16] T. Heinonen, P. Dastidar, H. Eskola, H. Frey, P. Ryymin and E. Laasonen, Applicability of
semi-automatic segmentation for volumetric analysis of brain lesions, Journal of Medical
Engineering And Technology, 22(4) (1998), 173{178.
[17] S. K. Jha and R. D. S. Yadava, Denoising by singular value decomposition and its application
to electronic nose data processing, IEEE Sensors Journal, 11(1) (2011), 35{44.
[18] L. Ji and H. Yan, An attractable snakes based on the greedy algorithm for contour extraction,
Pattern Recognition, 35(4) (2002), 791{806.
[19] Z. X. Ji, Q. S. Sun and D. S. Xia, A modi ed possibilistic fuzzy c-means clustering algo-
rithm for bias eld estimation and segmentation of brain MR image, Computerized Medical
Imaging and Graphics, 35(5) (2011), 383{397.
[20] L. Jui-Hsiang, T. Ming-Feng, C. Lumdo and C. C. P. Chen, Accurate and analytical statistical
spatial correlation modeling based on singular value decomposition for VLSI DFM applica-
tions, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,
29(4) (2010), 580-589.
[21] F. Kyoomarsi, H. Khosravi, E. Eslami and M. Davoudi, Extraction-based Text Summarization
Using Fuzzy Analysisn, Iranian Journal of Fuzzy Systems, 7(3) (2010), 15{32.
[22] Llado, A. Oliver, M. Cabezas, J. Freixenet, J. C. Vilanova, A. Quiles, L. Valls, L. Ramio-
Torrent and A. Rovira, Segmentation of multiple sclerosis lesions in brain MRI: a review of
automated approaches, Information Sciences, 186(1) (2012), 164{185.
[23] A. W. Liew and H. Yan, An adaptive spatial fuzzy clustering algorithm for 3-D MR image
segmentation, IEEE transactions on medical imaging, 22(9) (2003), 1063{1075.
[24] A. Liew and H. Yan, Current methods in the automatic tissue segmentation of 3D magnetic
resonance brain images, Current Medical Imaging Reviews, 2(1) (2006), 91{103.
[25] E. G. Mansoori, M. J. Zolghadri and S. D. Katebi, Using distribution of data to enhance
prformance of fuzzy classi cation systems, Iranian Journal of Fuzzy Systems, 4(1) (2007),
21{36.
[26] E. G. Mansoori, M. J. Zolghadri, S. D. Katebi, H. Mohabatkar,R. Boostani and M. H.
Sadreddini, Generating fuzzy for protein classi cation, Iranian Journal of Fuzzy Systems,
5(2) (2008), 21{33.
[27] T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: a survey,
Medical Image Analysis, 1(2) (1996), 91{108.
[28] S. B. Mehta, S. Chaudhury, A. Bhattacharyya and A. Jena, Handcrafted fuzzy rules for tissue
classi cation, Magnetic Resonance Imaging, 26(6) (2008), 815{823.
[29] F. Moayedi, R. bostani, A. R. Kazemi, S. Katebi and E. Dashti, Subclass Fuzzy-SVM classi er
as an ecient method to enhance the mass detection in mamograms, Iranian Journal of Fuzzy
Systems, 7(1) (2010), 15{31.
[30] W. J. Niessen, K. L. Vincken, J. Weickert, B. M. T. H. Romeny and M. A. Viergever, Multi-
scale segmentation of three-dimensional MR brain images, International Journal of Computer
Vision, 31(2) (1999), 185{202.
[31] D. L. Pham and J. L. Prince, An adaptive fuzzy c-means algorithm for image segmentation in
the presence of intensity inhomogeneities, Pattern Recognition Letters, 20(1) (1999), 57{68.
[32] D. L. Pham and J. L. Prince, Adaptive fuzzy segmentation of magnetic resonance images,
IEEE Transactions on Medical Imaging, 18(9) (1999), 737-752.
[33] D. L. Pham, C. Xu and J. L. Prince, A survey of current methods in medical image segmen-
tation, Annual Review of Biomedical Engineering, 2 (2000), 315{337.
[34] S. Prima, N. Ayache, T. Barrick and N. Roberts, Maximum likelihood estimation of the bias
eld in MR brain images: investigating di erent modelings of the imaging process, presented
at the Proceedings of the 4th International Conference on Medical Image Computing and
Computer-Assisted Intervention, Utrecht, The Netherlands, 2001.
[35] S. Ramathilagam, R. Pandiyarajan, A. Sathya, R. Devi and S. R. Kannan, Modi ed fuzzy c-
means algorithm for segmentation of T1-T2-weighted brain MRI, Journal of Computational
and Applied Mathematics, 235(6) (2011), 1578{1586.
[36] G. Shafer, A mathematical theory of evidence, Princeton University Press, Princeton., 1976.
[37] S. Shen, W. Sandham, M. Granat and A. Sterr, MRI fuzzy segmentation of brain tissue using
neighborhood attraction with neural-network optimization, IEEE Transactions on Information
Technology in Biomedicine : a Publication of the Ieee Engineering in Medicine and Biology
Society, 9(3) (2005), 459{67.
[38] A. Simmons, P. S. Tofts, G. J. Barker and S. R. Arridge, Sources of intensity nonuniformity
in spin echo images at 1.5 T, Magnetic Resonance in Medicine : ocial journal of the Society
of Magnetic Resonance in Medicine, 32(1) (1994), 121{8.
[39] M. Y. Siyal and L. Yu, An intelligent modi ed fuzzy c-means based algorithm for bias estima-
tion and segmentation of brain MRI, Pattern Recognition Letters, 26(13) (2005), 2052-2062.
[40] J. G. Sled, A. P. Zijdenbos and A. C. Evans, A nonparametric method for automatic correc-
tion of intensity nonuniformity in MRI data, IEEE Transactions On Medical Imaging, 17(1)
(1998), 87{97.
[41] P. Smets and R. Kennes, The transferable belief model, Arti cial Intelligence, 66 (2) (1994),
191{234.
[42] M. Styner, C. Brechbuhler, G. Szekely and G. Gerig, Parametric estimate of intensity inho-
mogeneities applied to MRI, IEEE Transactions on Medical Imaging, 19(3) (2000), 153{165.
[43] M. Tabassian, R. Ghaderi and R. Ebrahimpour, Combination of multiple diverse classi ers
using belief functions for handling data with imperfect labels, Expert Systems with Applica-
tions, 39(2) (2012), 1698{1707.
[44] M. Tabassian, R. Ghaderi and R. Ebrahimpour, Knitted fabric defect classi cation for uncer-
tain labels based on Dempster-Shafer theory of evidence, Expert Systems with Applications,
38(5) (2011), 5259{5267.
[45] L. Tzu-Chao, Switching-based lter based on Dempsters combination rule for image process-
ing, Information Sciences, 180(24) (2010), 4892{4908.
[46] v, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York,
1981.
[47] F. Valente, Multi-stream speech recognition based on DempsterShafer combination rule,
Speech Communication, 52(3) (2010), 213{222.
[48] J. Wang, J. Kong, Y. Lu, M. Qi and B. Zhang, A modi ed FCM algorithm for MRI brain
image segmentation using both local and non-local spatial constraints, Computerized Medical
Imaging and Graphics : the Ocial Journal of the Computerized Medical Imaging Society,
32(8) (2008), 685{698.
[49] R. R. Yager, J. Kacprzyk and M. Fedrizzi, Advances in the Dempster-Shafer theory of evi-
dence, New York ; Chichester: Wiley, 1994.
[50] D. Q. Zhang and S. C. Chen, A novel kernelized fuzzy c-means algorithm with application in
medical image segmentation, Arti cial Intelligence in Medicine, 32(1) (2004), 37-50.
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