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2011 | 21 | 1 | 203-209
Tytuł artykułu

KHM clustering technique as a segmentation method for endoscopic colour images

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.
Rocznik
Tom
21
Numer
1
Strony
203-209
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-02-08
poprawiono
2010-05-02
Twórcy
  • Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
autor
  • Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA.
  • Borsotti, M., Campadelli, P. and Schettini, R. (1998). Quantitative evaluation of color image segmentation results, Pattern Recognition Letters 19(8): 741-747.
  • Cheng, H., Jiang, X., Sun, Y. and Wang, J. (2001). Color image segmentation: Advances and prospects, Pattern Recognition 34(12): 2259-2281.
  • Frąckiewicz, M. and Palus, H. (2009a). Initialization methods for clustering in colour image quantization, Proceedings of the 7th Conference on Computer Methods and Systems (CMS'09), Cracow, Poland, pp. 469-472.
  • Frąckiewicz, M. and Palus, H. (2009b). KM and KHM clustering techniques: Computing acceleration by multithread programming, Proceedings of the 7th Conference on Computer Methods and Systems (CMS'09), Cracow, Poland, pp. 333-338.
  • Hamerly, G.J. (2003). Learning Structure and Concepts in Data through Data Clustering, Ph.D. thesis, University of California, San Diego, CA.
  • Linde, Y., Buzo, A. and Gray, R. (1980). An algorithm for vector quantizer design, IEEE Transactions on Communications 28(1): 84-95.
  • Lloyd, S. (1982). Least squares quantization in PCM, IEEE Transactions on Information Theory 28(2): 129-137.
  • MacQuenn, J. (1967). Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematics, Statistics, and Probabilities, Berkeley CA, USA, pp. 281-297.
  • Palus, H. (2006). Color image segmentation: Selected techniques, in R. Lukac and K. Plataniotis (Eds.), Color Image Processing: Methods and Applications, CRC Press, Boca Raton, FL, pp. 103-108.
  • Zhang, B. (2000). Generalized k-harmonic means-Boosting in unsupervised learning, Technical Report TR HPL-2000137, Hewlett Packard Labs, Palo Alto, CA.
  • Zhang, B., Hsu, M. and Dayal, U. (1999). K-harmonic means - Data clustering algorithm, Technical Report TR HPL1999-124, Hewlett Packard Labs, Palo Alto, CA.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i1p203bwm
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