All Title Author
Keywords Abstract


Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

DOI: 10.1155/2013/435497

Full-Text   Cite this paper   Add to My Lib

Abstract:

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images. 1. Introduction Image segmentation is a very important operation in the process of treatment and analyzing images, and it is widely used in the different fields: pattern recognition, remote sensing, artificial intelligence, medical imaging, and so forth. The field of medical imaging includes several types of images: radiography (X-ray), ultrasound and magnetic resonance image [1–4]. These images are a very complex data, so their analysis is a challenge for researches. In the literature, there are several methods that can segment these images. We can group them in four classes. The first one is the Thresholding; it allows to find the optimal threshold value, in order to extract the background objects in the image. In general, this approach is very sensitive to noise and ignores the spatial parameters [5, 6]. The second approach is the Contour; it allows to detect the contour of the image. This method is easy to implement, but unfortunately it is very sensitive to the noise and also to the parameters initialization, which means that it is mostly used with a pretreatment filter [7–10]. The third approach is the Region, which generates some methods: growing region (called ascendant) and splitting/merging (called descendants); this approach is very sensitive to the initial parameters and to the noise [11–13]. The last approach is the Clustering; it is a very important operation in the process and data analysis, and it allows creating the homogeneous partitions using a similarity criterion [3, 4, 14–33]. In this work, we are interested in clustering segmentation using the possibility theory

References

[1]  C. S. Drapaca, V. Cardenas, and C. Studholme, “Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets,” Computer Vision and Image Understanding, vol. 100, no. 3, pp. 312–329, 2005.
[2]  M. Kamber, R. Shinghal, D. L. Collins, G. S. Francis, and A. C. Evans, “Model-based segmentation of multiple sclerosis lesions in magnetic resonance brain images,” IEEE Transactions on Medical Imaging, vol. 14, no. 3, pp. 442–453, 2000.
[3]  N. El Harchaoui, S. Bara, M. Ait-Kerroum, A. Hammouch, M. Ouadou, and D. Aboutajdine, “Improved fuzzy clustering approach: application to medical image MRI,” in Proceedings of the IEEE International Conference in Complex Systems, pp. 1–6, 2012.
[4]  D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, “Multimodal classification of Alzheimer's disease and mild cognitive impairment,” NeuroImage, vol. 55, no. 3, pp. 856–867, 2011.
[5]  Y. Qiao, Q. Hu, G. Qian, S. Luo, and W. L. Nowinski, “Thresholding based on variance and intensity contrast,” Pattern Recognition, vol. 40, no. 2, pp. 596–608, 2007.
[6]  Z. Jun and H. Jinglu, “Image segmentation based on 2D Otsu method with histogram analysis,” in Proceedings of the International Conference on Computer Science and Software Engineering (CSSE '08), pp. 105–108, Wuhan, China, December 2008.
[7]  T. Pavlidis and Y.-T. Liow, “Integrating region growing and edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 225–233, 1990.
[8]  L. D. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2-D and 3-D images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1131–1147, 1993.
[9]  T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001.
[10]  L. Najman and M. Schmitt, “Geodesic saliency of watershed contours and hierarchical segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 12, pp. 1163–1173, 1996.
[11]  M. Tabb and N. Ahuja, “Multiscale image segmentation by integrated edge and region detection,” IEEE Transactions on Image Processing, vol. 6, no. 5, pp. 642–655, 1997.
[12]  J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1454–1466, 2001.
[13]  S.-Y. Wan and W. E. Higgins, “Symmetric region growing,” IEEE Transactions on Image Processing, vol. 12, no. 9, pp. 1007–1015, 2003.
[14]  M. Ait Kerroum, A. Hammouch, and D. Aboutajdine, “Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification,” Pattern Recognition Letters, vol. 31, no. 10, pp. 1168–1174, 2010.
[15]  M. Ait kerroum, A. Hammouch, and D. Aboutajdine, “Input textural feature selection by mutual information for multispectral image classification,” International Journal of Signal Processing, vol. 6, article 1, 2010.
[16]  D. Vidal-Madjar, “Application of dempster-shafer evidence theory to unsupervised classification in multisource remote sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 4, pp. 1018–1031, 1997.
[17]  R. Xu and D. Wunsch II, “Survey of clustering algorithms,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645–678, 2005.
[18]  K. Wagsta, C. Cardie, S. Rogers, and S. Schroedl, “Constrained k-means clustering with background knowledge,” in Proceedings of the International Conference on Machine Learning, pp. 557–584, 2001.
[19]  L. Jing, M. K. Ng, and J. Z. Huang, “An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 8, pp. 1026–1041, 2007.
[20]  F. Hppner, F. Klawonn, R. Kruse, and T. Runkler, “Fuzzy Cluster Analysis Methods for Classication,” in Data Analysis and Image Recognition, John Wiley and Sons, LTD, 1999.
[21]  J. C. Bezdek, “Cluster validity with fuzzy sets,” Journal of Cybernetics, vol. 3, no. 3, pp. 58–73, 1973.
[22]  J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981.
[23]  R. Krishnapuram and J. M. Keller, “Possibilistic approach to clustering,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, pp. 98–110, 1993.
[24]  K. P. Detroja, R. D. Gudi, and S. C. Patwardhan, “A possibilistic clustering approach to novel fault detection and isolation,” Journal of Process Control, vol. 16, no. 10, pp. 1055–1073, 2006.
[25]  Y. Jiang, B. Cukic, and Y. Ma, “Techniques for evaluating fault prediction models,” Empirical Software Engineering, vol. 13, no. 5, pp. 561–595, 2008.
[26]  A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood for incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, vol. 39, no. 1, pp. 1–38, 1977.
[27]  C. Biernacki, G. Celeux, and G. Govaert, “Assessing a mixture model for clustering with the integrated completed likelihood,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 7, pp. 719–725, 2000.
[28]  N. El Harchaoui, S. Bara, M. Ait-Kerroum, A. Hammouch, M. Ouadou, and D. Aboutajdine, “An improved fuzzy clustering approach using possibilist c-means algorithm: application to medical image MRI,” in Proceedings of the IEEE Colloquium in Information Science and Technology (CIST '12), pp. 117–122, 2012.
[29]  H. Timm, C. Borgelt, C. D?ring, and R. Kruse, “An extension to possibilistic fuzzy cluster analysis,” Fuzzy Sets and Systems, vol. 147, no. 1, pp. 3–16, 2004.
[30]  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, pp. 517–530, 2005.
[31]  C. Blake, E. Keogh, and C. J. Merz, UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, Calif, USA, 1998, http://www.ics.uci.edu/mlearn/MLRepository.html.
[32]  Z. Huang and M. K. Ng, “A fuzzy k-modes algorithm for clustering categorical data,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 4, pp. 446–452, 1999.
[33]  J. C. Dunn, “Fuzzy relative of the isodata process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973.
[34]  L. A. Zadeh, “Fuzzy sets and their application to pattern classification and clustering analysis,” in Classification and Clustering, J. V. Ryzin, Ed., pp. 251–282, 1977.
[35]  E. H. Ruspini, “A new approach to clustering,” Information and Control, vol. 15, no. 1, pp. 22–32, 1969.
[36]  L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[37]  P. J. Rousseeuw, “Discussion: fuzzy clustering at the intersection,” Technometrics, vol. 37, no. 3, pp. 283–285, 1995.
[38]  P. J. Rousseeuw and B. C. Van Zomeren, “Unmasking multivariate outliers and leverage points,” Journal of the American Statistical Association, vol. 85, no. 411, pp. 633–639, 1990.
[39]  L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley and Sons, 1990.
[40]  M. Barni, V. Cappellini, and A. Mecocci, “Comments on ‘a possibilistic approach to clustering’,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 3, pp. 393–396, 1996.
[41]  BrainWeb, “Simulated Brain Database. McConnell Brain Imaging Center. Montreal Neurological Institute, McGill University,” http://www.bic.mni.mcgill.ca/brainweb.
[42]  “Internet brain segmentation repository (IBSR),” http://www.cma.mgh.harvard.edu/ibsr/.

Full-Text

comments powered by Disqus