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Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means

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465--481
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bibliogr. 41 poz., fot.,tab.,wykr.
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Bibliografia
  • [1] Andrew J. Worth, Nikos Makris, James W. Meyer, Verne S. Caviness, Jr. and David N. (1997) Kennedy "Automated Segmentation of Brain Exterior in MR Images Driven by Empirical Procedures and Anatomical Knowledge. XVth International Conference on Information Processing in Medical Imaging (IPMI'97), Poultney, Vermont, vol. 1230, pp. 99-112.
  • [2] Ayman El-Baz, Aly A. Farag, Asem Ali, Georgy L. Gimel'farb,Manuel Casanova, (2006) A Framework for Unsupervised Segmentation ofMulti-modalMedical Images. Proc. of the Second InternationalWorkshop of Computer Vision Approaches to Medical Image Analysis(CVAMIA'06), Graz, Austria, pp. 120-131, May 2006.
  • [3] Bezdek J, Hall L, and Clarke L.(1993) Review of MR image segmentation using pattern recognition. Med. Phys vol. 20, pp.1033-1048, 1993.
  • [4] Caviness V. S., Filipek Jr., P. A., and Kennedy D. N., (1989)Magnetic resonance technology in human brain science: A blueprint for a program based uponmorphometry" Brain Dev, vol. 11, pp. 1-13.
  • [5] Kai Xiao, Sooi Hock Ho, and Qussay Salih (2007) A Study: Segmentation of Lateral Ventricles in Brain MRI using Fuzzy C-Means Clustering with Gaussian Smoothing. In the proceeding of the Joint Rough Set Symposium JRS2007 (Rough Sets, Fuzzy Sets, Data Mining & Granular Computing RSFDGrC07) MARS Discovery District Centre, Toronto, Canada May 14-16, pp. 161-170.
  • [6] Liu, Jian-Guo, Udupa, Jayaram K.; Hackney, David; Moonis, Gul (2001) Brain tumor segmentation in MRI by using the fuzzy connectedness method. Proc. SPIE Vol. 4322, p. 1455-1465,: Image Processing, Milan Sonka; Kenneth M. Hanson; Eds. Medical Imaging 2001.
  • [7] Rivera-Rovelo Jorge and Bayro-Corrochano Eduardo (2007) Medical image segmentation: volume representation and registration using spheres in the geometric algebra framework. Pattern Recognition, vol. 40(1), pp: 171-188.
  • [8] Brandt ME, Bohan TP, Kramer LA, Fletcher JM. (1994) Estimation of CSF, white matter and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Comput Med Imaging Graph; 18, pp.25-34, 1994.
  • [9] Pham DL, Prince JL. (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans on Med Imaging vol. 18, pp: 737-752, 1999.
  • [10] Wu Y, Pohl K,Warfield SK, Cuttmann CRG. (2003) Automated Segmentation of Cerebral Ventricular Compartments. Proceedings of International Society for Magnetic Resonance in Medicine Eleventh Scientific Meeting and Exhibition (ISMRM'2003), Toronto, Ontaril, Canada. Program No. 906, 10 -16 July 2003.
  • [11] Andrew J. Worth, Nikos Makris, Mark R. Patti, Julie M. Goodman, Elizabeth A. Hoge, Verne S. Caviness, Jr. , David N. Kennedy. (1997) Precise Segmentation of the Lateral Ventricles and Caudate Nucleus in MR Brain Images using Anatomically Driven Histograms, IEEE Transactions on Medical Imaging, 1997.
  • [12] Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ. (2006) Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, pp.30:9-15.
  • [13] Pedrycz W. and Waletzky J., (1997) Fuzzy clustering with partial supervision. IEEE Trans. Syst., Man, Cybern., vol. 27, pp. 787-795, Oct. 1997.
  • [14] Singh, M.; Patel, P.; Khosla, D.; Kim, T. (1996) Segmentation of functional MRI by K-means clustering. IEEE Transactions on Nuclear Science Volume 43, Issue 3, pp.2030 - 2036.
  • [15] Alirezaie J., Jernigan, M.E. and Nahmias, C. (1998) Automatic segmentation of cerebral MR images using artificial neural networks. IEEE Trans on Nuclear Science, Vol. 45, No. 4, pp. 2174 - 2182, 1998
  • [16] Chen, T.; Huang, T.S. and Liang, Z.-P. (2004) Segmentation of brain MR images using hidden Markov random field model with weighting neighborhood system. Nuclear Science Symposium Conference Record, 2004 IEEE Volume 5, 16-22, pp: 3209 - 3212, Oct. 2004.
  • [17] Wei Sun and Yaonan Wang, (2005) Segmentation Method of MRI Using Fuzzy Gaussian Basis Neural Network. Neural Information Processing - Letters and Reviews, vol.8, No.2, pp: 19-24, August 2005
  • [18] Zadeh L.A. (1965) Fuzzy Sets. Information and Control, vol.8, pp.338-353, 1965.
  • [19] Kerre E. and Nachtegael M. (2000) Fuzzy techniques in image processing: Techniques and applications. Studies in Fuzziness and Soft Computing, vol. 52, Physica Verlag, Heidelberg.
  • [20] NachtegaelM. Van-Der-WekenM. Van-De-VilleD. Kerre D. PhilipsW. and Lemahieu I. (2001)An overview of classical and fuzzy-classical filters for noise reduction. In: 10th International IEEE Conference on Fuzzy Systems FUZZ-IEEE'2001,Melbourne, Australia, pp.3-6.
  • [21] Sankar K. Pal (2001) Fuzzy image processing and recognition: Uncertainties handling and applications, Internat. J. Image Graphics 1 (2001) (2), pp. 169195.
  • [22] Hassanien A.E. Ali J.M. and Hajime N. (2004) Detection of spiculated masses in Mammograms based on fuzzy image processing. In: 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC2004, Zakopane, Poland, June 7-11. LNAI, Springer, vol.3070, pp.1002-1007.
  • [23] Selvathi, D., Arulmurgan, A., Thamarai Seivi, S., and Alagappan, S, (2005) MRI image segmentation using unsupervised clustering techniques. Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 16-18 Aug, pp. 105-110.
  • [24] Tizhoosh H.R (2000) Fuzzy image enhancement: an overview. Kerre E. Nachtegael M. (eds.), Fuzzy Techniques in Image Processing, Springer, Studies in Fuzziness and Soft Computing, pp.137-171.
  • [25] Bouchachia A, and Pedrycz W. (2006) Enhancement of fuzzy clustering by mechanisms of partial supervision. Fuzzy Sets and Systems, 157, pp.1733-1759.
  • [26] Cai W, Chen S, Zhang D. (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition; vol. 40, pp. 825-838, 2007
  • [27] Signal-to-noise ratio, Wikipedia Clustering - Fuzzy C-Means, Dipartimento di Elettronica e Informazione, Politecnico di Milano ttp://www.elet.polimi.it/upload/matteucc/Clustering/
  • [28] Clustering - Fuzzy C-Means, Dipartimento di Elettronica e Informazione, Politecnico di Milano http://www.elet.polimi.it/upload/matteucc/Clustering/
  • [29] Goldberger J., Gordon S. and Greenspan H. (2006) Unsupervised image-set clustering using an information theoretic framework. IEEE Trans. on Image Processing, 15(2), pp.449-458.
  • [30] Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T. (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002;21:193-9.
  • [31] Dulyakarn P., Rangsanseri Y. (2001) Fuzzy C-Means clustering using spatial information with application to remote sensing. 22nd Asian Conference on Remote Sensing, 5-9, Sinagpore (2001)
  • [32] Data clustering,Wikipedia http://en.wikipedia.org/wiki/
  • [33] Gaussian blur ,Wikipedia http://en.wikipedia.org/wiki/
  • [34] Spatial Filters - Gaussian Smoothing, School of informatics, The University of Edinburgh http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm
  • [35] Bezdek JC., Cluster validity with fuzzy sets, J Cybern, 3, 1974, 58-73.
  • [36] Bezdek JC. (1975)Mathematicalmodels for systematic and taxonomy. In: proceedings of eigth international conference on numerical taxonomy, San Francisco;, pp.143-166.
  • [37] Windham M. P., (1982) Cluster validity for fuzzy c-means clustering algorithm. IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-4, no. 4.
  • [38] Xuanli Lisae Xie and Gerardo Beni., (1991) A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 13 , Issue 8, pp.841 - 847, August 1991.
  • [39] Chen CF, Lee JM. (2001) The Validity Measurement of Fuzzy C-Means Classifer for Remotely Sensed Images. Asian Conference on Remote Sensing, 2001.
  • [40] Hoppner, F. et al., (1999). Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. JohnWiley & Sons, England.
  • [41] The Whole Brain Atlas, Harvard Medical School http://www.med.harvard.edu/AANLIB/home.html
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0018-0049
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