KHM clustering technique as a segmentation method for endoscopic colour images

Mariusz Frąckiewicz; Henryk Palus

doi:10.2478/v10006-011-0015-0

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KHM clustering technique as a segmentation method for endoscopic colour images

International Journal of Applied Mathematics and Computer Science

Volume 21 (2011): Issue 1 (March 2011)

By: Mariusz Frąckiewicz and Henryk Palus

Open Access

|Mar 2011

Abstract

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.

References

Bezdek, J. C. (1981)., Kluwer Academic Publishers, Norwell, MA.
Open DOI Search in Google Scholar
Borsotti, M., Campadelli, P. and Schettini, R. (1998). Quantitative evaluation of color image segmentation results,(8): 741-747.
Open DOI Search in Google Scholar
Cheng, H., Jiang, X., Sun, Y. and Wang, J. (2001). Color image segmentation: Advances and prospects,(12): 2259-2281.
Open DOI Search in Google Scholar
Frąckiewicz, M. and Palus, H. (2009a). Initialization methods for clustering in colour image quantization,, pp. 469-472.
Search in Google Scholar
Frąckiewicz, M. and Palus, H. (2009b). KM and KHM clustering techniques: Computing acceleration by multithread programming,, pp. 333-338.
Search in Google Scholar
Hamerly, G. J. (2003)., Ph.D. thesis, University of California, San Diego, CA.
Search in Google Scholar
Linde, Y., Buzo, A. and Gray, R. (1980). An algorithm for vector quantizer design,(1): 84-95.
Open DOI Search in Google Scholar
Lloyd, S. (1982). Least squares quantization in PCM,(2): 129-137.
Open DOI Search in Google Scholar
MacQuenn, J. (1967). Some methods for classification and analysis of multivariate observations,, pp. 281-297.
Search in Google Scholar
Palus, H. (2006). Color image segmentation: Selected techniques,R. Lukac and K. Plataniotis (Eds.),, CRC Press, Boca Raton, FL, pp. 103-108.
Open DOI Search in Google Scholar
Zhang, B. (2000). Generalized k-harmonic means—Boosting in unsupervised learning,, Hewlett Packard Labs, Palo Alto, CA.
Search in Google Scholar
Zhang, B., Hsu, M. and Dayal, U. (1999). K-harmonic means—Data clustering algorithm,, Hewlett Packard Labs, Palo Alto, CA.
Search in Google Scholar