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Performance Analysis of Unsupervised Probabilistic, Possibilistic & Plausibilistic Clustering Algorithms for Brain Tissue Segmentation

© 2010 by IJCA Journal
Number 2 - Article 8
Year of Publication: 2010
Ghanshyam D. Parmar
Suman K. Mitra

Ghanshyam D.Parmar Suman K.Mitra. Article: Performance Analysis of Unsupervised Probabilistic, Possibilistic & Plausibilistic Clustering Algorithms for Brain Tissue Segmentation. IJCA,Special Issue on CASCT (2):93–98, 2010. Published By Foundation of Computer Science. BibTeX

	author = {Suman K.Mitra, Ghanshyam D.Parmar},
	title = {Article: Performance Analysis of Unsupervised Probabilistic, Possibilistic & Plausibilistic Clustering Algorithms for Brain Tissue Segmentation},
	journal = {IJCA,Special Issue on CASCT},
	year = {2010},
	number = {2},
	pages = {93--98},
	note = {Published By Foundation of Computer Science}


Segmentation of brain tissues is one important process prior to many analysis and visualization tasks for magnetic resonance (MR) images. Clustering is one of the unsupervised techniques for doing the segmentation. Clustering is done with probabilistic, possibilistic and plausibilistic approaches. Most of segmentation techniques have relied on multi channel characteristics of MR images while a few studies have reported segmentation from single channel MR images. Owing to operator performance, limitation of equipment and environmental conditions MR images contain noise. This noise can lead to serious inaccuracies in the segmentation result. We conduct the research in measuring the performance of crisp and fuzzy clustering algorithms with probabilistic, possibilistic and plausibilistic approach in different noise level for single channel MR image. To validate the accuracy and robustness of the result of clustering algorithms we carried out experiments on simulated MR brain scans. The performance of algorithms is analyzed form three measures namely: number of iterations required, misclassification error and per class (tissue) misclassification error in different noise level present in the single-channel MR image.


  • P. C. Lauterbur, Image formation by induced local interactions: Examples employing nuclear magnetic resonance, Nature 242 (5394) (1973) 190-191.
  • P. C. Lauterbur, Magnetic resonance zeugmatography, Pure Appl. Chem. 40 (1-2) (1974) 149-157.
  • L. F. Squire, Fundamentals of radiology, 4th Edition, Harvard University Press, 1988
  • H. Damasio, Human brain anatomy in computerized images, 2nd Edition, Oxford University Press US, 2005.
  • P. B. Henri M. Duvernoy, The human brain: surface, three-dimensional sectional anatomy with MRI, and blood supply, 2nd Edition, Springer, 1999.
  • J. Nolte, The human brain: an introduction to its functional anatomy, Mosby, 1981.
  • C. R. Jack, Brain and cerebrospinal fluid volume: Measurement with mr imaging, Radiol. 178) (1991) 22{24.
  • T. E. Schlaepfer, G. J. Harris, A. Y. Tien, L. W. Peng, S. Lee, B. Federman, G. A. Chase, P. E. Barta, G. D. Pearlson, Decreased regional cortical gray matter volume in schizophrenia, American Journal of Psychiatry 151 (6) (1994) 842-848.
  • W. Oslen, F. Longo, D. Mills, D. Norman, White matter disease in aids: Finding at MR imaging, Neuroradiol 169 (1988) 445-448.
  • K. Fitzgerald, Medical electronics, IEEE Spectrum 28 (1) (1991) 76-78.
  • R. Robb, Three-Dimensional Biomedical Imaging, New York: VCH, 1995.
  • M. Brummer, R. Mersereau, R. Eisner, R. Lewine, Automatic detection of brain contours in MRI data sets, IEEE Trans. Med. Imag. 12 (2) (1993) 153-166.
  • A. K. H. Miller, R. L. Alston, J. A. N. Corsellis, Variation with age in the volumes of gray and white matter in the cerebral hemispheres of man: Measurement with an image analyzer, Neuropathol., Appl. Neurobiol. 6 (1980) 119-132.
  • T. Autti, R. Raininko, S. Vanhanen, M. Kallio, Santavuori, MRI of normal brain from early childhood to middle age, Neuroradiol 36 (1994) 644-648.
  • D. G. M. Murphy, C. DeCarli, M. B. Schapiro, S. Rapoport, Horwitz, Age-related differences in volumes of subcortical nuclei, brain matter, and cerebrospinal fluid in healthy men as measured with magnetic resonance imaging, Arch. Neurol. 49 (1992) 839-845.
  • K. O. Lim, R. B. Zipursky, M. C. Watts, A. Pfefferbaum, Decreased gray matter in normal aging: An in vivo magnetic resonance study, J. Gerontol.: Biolog. Sci. 47 (1) (1992) B26-30.
  • R. H. Hashemi, W. G. Bradley, C. J. Lisanti, MRI: the basics, 2nd Edition, Lippincott Williams & Wilkins, 2004.
  • D. W. McRobbie, E. A. Moore, M. J. Graves, MRI from picture to proton, Cambridge University Press, 2003.
  • J. Rajapakse, J. Giedd, J. Rapoport, Statistical approach to segmentation of single-channel cerebral MR images, IEEE Transactions on Medical Imaging 16 (2) (1997) 176-186.
  • R. Fisher, On an absolute criterion for ftting frequency curves, Statistical Science 12 (1) (1997) 39-41.
  • J. Aldrich, RA Fisher and the making of maximum likelihood 1912-1922, Statistical Science 12 (3) (1997) 162-176.
  • A. Edwards, Likelihood, Cambridge Univ Pr, 1984.
  • R. Royall, Statistical evidence: a likelihood paradigm, CRC Press, 1997.
  • A. Dempster, N. Laird, D. Rubin, et al., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological) 39 (1) (1977) 1-38.
  • R. Redner, H. Walker, Mixture densities, maximum likelihood and the EM algorithm, SIAM review 26 (2) (1984) 195-239.
  • J. C. Bezdek, J. C. Dunn, Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions, IEEE Trans. Comput. 24 (8) (1975) 835-838.
  • I. Gath, A. Geva, Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7) (1989) 773-780.
  • L. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy sets and systems 100 (1999) 9-34.
  • L. Zadeh, A theory of approximate reasoning, Machine intelligence 9 (1) (1979) 149-194.
  • L. Zadeh, Fuzzy sets*, Information and control 8 (3) (1965) 338-353.
  • R. Krishnapuram, J. Keller, A possibilistic approach to clustering, Fuzzy Systems, IEEE Transactions on 1 (2) (1993) 98-110.
  • N. Pal, K. Pal, J. Bezdek, A mixed c-means clustering model, in: Fuzzy Systems, 1997., Proceedings of the Sixth IEEE Inter-national Conference on, Vol. 1, (1997), pp. 11-21.
  • A. Dempster, Upper and lower probabilities induced by a multi-valued mapping, The Annals of Mathematical Statistics 38 (2) (1967) 325-339.
  • G. Shafer, A mathematical theory of evidence, Princeton university press Princeton, NJ, 1976.
  • P. Smets, The transferable belief model for quantified belief representation, Handbook of defeasible reasoning and uncertainty management systems 1 (1998) 267-301.
  • T. Denœux, M. Masson, Evclus: evidential clustering of proximity data, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (1) (2004) 95-109.
  • J. Sammon, A nonlinear mapping for data structure analysis, IEEE Transactions on computers 18 (5) (1969) 401-409.
  • M. Masson, T. Denœux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognition 41 (4) (2008) 1384-1397.
  • W. Wang, Y. Zhang, On fuzzy cluster validity indices, Fuzzy Sets Syst. 158 (19) (2007) 2095-2117.
  • C. A. Cocosco, V. Kollokian, R. K. S. Kwan, G. B. Pike, A. C. Evans, Brainweb: Online interface to a 3d MRI simulated brain database, NeuroImage 5 (1997) 425.
  • R. K. Kwan, A. C. Evans, G. B. Pike, MRI simulation based evaluation of image-processing and classification methods., IEEE Trans Med Imaging 18 (11) (1999) 1085-1097.
  • R. Kwan, A. C. Evans, G. B. Pike, An extensible MRI simulator for post-processing evaluation, Springer-Verlag, 1996, pp. 135-140
  • D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, A. C. Evans, Design and construction of a realistic digital brain phantom, IEEE Trans. Med. Imaging (3) (1998) 463-468.