OJMI  Vol.3 No.4 , December 2013
An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation
ABSTRACT

In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (FCM) and possiblistic c-means (PCM) functions. It is realized by modifying the objective function of the conventional PCM algorithm with Gaussian exponent weights to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. For that, the proposed algorithm is capable to avoid various problems of existing fuzzy clustering methods that solve the defect of noise sensitivity and overcomes the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments by applying them to the challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets and by comparison with other state of the art algorithms. The experimental results show that the proposed method produces accurate and stable results.


Cite this paper
E. Allam Zanaty, "An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation," Open Journal of Medical Imaging, Vol. 3 No. 4, 2013, pp. 125-135. doi: 10.4236/ojmi.2013.34020.
References
[1]   R. Bakshi, S. Ariyaratana, R. H. B. Benedict and L. Jacobs, “Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging Detects Cortical and Juxtacortical Multiple Sclerosis Lesions,” Archives of Neurology, Vol. 58, No. 5, 2001, pp. 742-748.
http://dx.doi.org/10.1001/archneur.58.5.742

[2]   A. Ayman, T. Funatomi, M. Minoh, E. A. Zanaty, T. Okada, K. Togashi, T. Sakai and S. Yamada, “New Region Growing Segmentation Technique for MR Images with Weak Boundaries,” IEICE Conference MI2010-79, Japan, 2010, pp. 71-76.

[3]   H.-R. Wang, J.-L. Yang, H.-J. Sun, D. Chen and X.-L. Liu, “An Improved Region Growing Method for Medical Image Selection and Evaluation Based on Canny Edge Detection,” Management and Service Science (MASS), Wuhan, 12-14 August 2011, pp. 1-4.

[4]   M. N. Mubarak, M. M. Sathik, S. Z. Beevi and K. Revathy, “A Hybrid Region Growing Algorithm for Medical Image Segmentation,” International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, No. 3, 2012.

[5]   K. K. L. Wong, J. Y. Tu, R. M. Kelso, S. G. Worthley, P. Sanders, J. Mazumdar and D. Abbott, “Cardiac Flow Component Analysis,” Medical Engineering & Physics, Vol. 32, No. 2, 2010, pp. 174-188.
http://dx.doi.org/10.1016/j.medengphy.2009.11.007

[6]   E. A. Zanaty, “An Approach Based on Fusion Concepts for Improving Brain Magnetic Resonance Images (MRIs) Segmentation,” Journal of Medical Imagining and Health Informatics, Vol. 3, No. 1, 2013, pp. 30-37.
http://dx.doi.org/10.1166/jmihi.2013.1122

[7]   E. A. Zanaty and A. S. Ghiduk, “A Novel Approach for Medical Image Segmentation Based on Genetic and Seed Region Growing Algorithms,” Journal of Computer Science and Information Systems, Vol. 10, No. 3, 2013.

[8]   E. A. Zanaty and A. Afifi, “A Watershed Approach for Improving Medical Image Segmentation,” Computer Methods in Biomechanics and Biomedical Engineering, Vol. 16, No. 12, 2012, pp. 1262-1272.

[9]   M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag and T. Moriarty, “A Modified Fuzzy c-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data,” IEEE Transactions on Medical Imaging, Vol. 21, No. 3, 2002, pp. 193-199.
http://dx.doi.org/10.1109/42.996338

[10]   T. N. Pappas, “An Adaptive Clustering Algorithm for Image Segmentation,” IEEE Transactions on Signal Processing, Vol. 40, No. 4, 1992, pp. 901-914.
http://dx.doi.org/10.1109/78.127962

[11]   E. A. Zanaty and S. Aljahdali, “Automatic Fuzzy Algorithms for Reliable Image Segmentation,” International Society for Computers and Their Applications, Vol. 19, No. 3, 2012, pp. 166-175.

[12]   Z. M. Wang, Y. C. Soh, Q. Song and K. Sim, “Adaptive Spatial Information-Theoretic Clustering for Image Segmentation,” Pattern Recognition Letters, Vol. 42, No. 9, 2009, pp. 2029-2044.
http://dx.doi.org/10.1016/j.patcog.2009.01.023

[13]   J. H. Xue, A. Pizurica, W. Philips, E. Kerre, R. Van de Walle and I. Lemahieu, “An Integrated Method of Adaptive Enhancement for Unsupervised Segmentation of MRI Brain Images,” Pattern Recognition Letters, Vol. 24, No. 15, 2003, pp. 2549-2560.
http://dx.doi.org/10.1016/S0167-8655(03)00100-4

[14]   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, Vol. 30, No. 1, 2006, pp. 9-15.
http://dx.doi.org/10.1016/j.compmedimag.2005.10.001

[15]   S. H. Lee and M. M. Crawford, “Unsupervised Multistage Image Classification Using Hierarchical Clustering with a Bayesian Similarity Measure,” IEEE Transactions on Image Processing, Vol.14, No. 3, 2005, pp. 312-320.

[16]   A. W. C. Liew, S. H. Leung and W. H. Lau, “Fuzzy Image Clustering Incorporating Spatial Continuity,” IEE Proceedings of Vision, Image and Signal Processing, Vol. 147, No. 2, 2000, pp. 185-192.
http://dx.doi.org/10.1049/ip-vis:20000218

[17]   S. Roy, H. K. Agarwal, A. Carass, Y. Bai, D. L. Pham and J. L. Prince, “Fuzzy c-Means with Variable Com- pactness,” IEEE International Symposium on Biomedical Imaging, 2008, pp. 452-455.

[18]   D. Pham and J. Prince, “Adaptive Fuzzy Segmentation of Magnetic Resonance Images,” IEEE Transactions on Medical Imaging, Vol. 18, No. 9, 1999, pp. 737-752.
http://dx.doi.org/10.1109/42.802752

[19]   A. W. C. Liew and H. Yan, “An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation,” IEEE Transactions on Medical Imaging, Vol. 22, No. 9, 2003, pp. 1063-1075.
http://dx.doi.org/10.1109/TMI.2003.816956

[20]   L. Szilágyi, S. M. Szilágyi and Z. Benyó, “A Modified FCM Algorithm for Fast Segmentation of Brain MR Images,” ICIARLNCS, Vol. 4633, 2007, pp. 866-877.

[21]   B. Y. Kang, D. W. Kim and Q. Li, “Spatial Homogeneity-Based Fuzzy c-Means Algorithm for Image Segmentation,” FSKDLNAI, Vol. 3613, 2005, pp. 462-469.

[22]   J. Z. Wang, J. Kong, Y. H. Lu, M. Qi and B. X. Zhang, “A Modified FCM Algorithm for MRI Brain Image Seg- mentation Using Both Local and Non-Local Spatial Constrains,” Computerized Medical Imaging and Graphics, Vol. 31, No. 8, 2008, pp. 685-698.
http://dx.doi.org/10.1016/j.compmedimag.2008.08.004

[23]   Z. Ji, Q. Sun and D. Xia, “A Modified Possibilistic Fuzzy c-Means Clustering Algorithm for Bias Field Estimation and Segmentation of Brain MR Image,” Computerized Medical Imaging and Graphics, Vol. 35, No. 5, 2011, pp. 383-397.
http://dx.doi.org/10.1016/j.compmedimag.2010.12.001

[24]   A. Rajendran and R. Dhanasekaran, “MRI Brain Image Tissue Segmentation Analysis Using Possibilistic Fuzzy c-Means Method,” International Journal on Computer Science and Engineering, Vol. 3, No. 12, 2011, pp. 3832- 3836.

[25]   N. R. Pal, K. Pal and J. C. Bezdek, “A Mixed c-Means Clustering Model,” IEEE International Conference on Fuzzy Systems, Vol. 1, 1997, pp. 11-21.

[26]   N. R. Pal, K. Pal, J. M. Keller and J. C. Bezdek, “A Possibilistic Fuzzy c-Means Clustering Algorithm,” IEEE Transactions on Fuzzy Systems, Vol. 13, No. 4, 2005, pp. 517-530. http://dx.doi.org/10.1109/TFUZZ.2004.840099

[27]   H. Timm, C. Borgelt, C. Doring and R. Kruse, “An Ex- tension to Possibilistic Fuzzy Cluster Analysis,” Fuzzy Sets and Systems, Vol. 147, No. 1, 2004, pp. 3-16.
http://dx.doi.org/10.1016/j.fss.2003.11.009

[28]   J. S. Zhang and Y. W. Leung, “Improved Possibilistic c-Means Clustering Algorithms,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 2, 2004, pp. 209-217.
http://dx.doi.org/10.1109/TFUZZ.2004.825079

[29]   W. L. Hung, M. Yang and D. Chen, “Parameter Selection for Suppressed Fuzzy c-Means with an Application to MRI Segmentation,” Pattern Recognition Letters, Vol. 27, No. 5, 2006, pp. 424-438.

[30]   Brain Web, “Simulated Brain Database,” Mcconnell Brain Imaging Centre, Montreal Neurological Institute, McGill University.
http://brainweb.bic.mni.mcgill.ca/brainweb/

[31]   Z. H. Yin, Y. G. Tang, F. C. Sun and Z. Q. Sun, “Fuzzy Clustering with Novel Serable Criterion,” Tsinghua Science and Technology, Vol. 11, No. 1, 2006, pp. 50-53.
http://dx.doi.org/10.1016/S1007-0214(06)70154-7

[32]   A. P. Zijdenbos, “MRI Segmentation and the Quantification of White Matter Lesions,” PhD Thesis, Vanderbilt University, Nashville, 1994.

 
 
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