KHM clustering technique as a segmentation method for endoscopic colour images

Mariusz Frąckiewicz; Henryk Palus

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 1, page 203-209
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Mariusz Frąckiewicz, and Henryk Palus. "KHM clustering technique as a segmentation method for endoscopic colour images." International Journal of Applied Mathematics and Computer Science 21.1 (2011): 203-209. <http://eudml.org/doc/208034>.

@article{MariuszFrąckiewicz2011,
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.},
author = {Mariusz Frąckiewicz, Henryk Palus},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {biomedical colour image segmentation; k-harmonic means technique; k-means technique},
language = {eng},
number = {1},
pages = {203-209},
title = {KHM clustering technique as a segmentation method for endoscopic colour images},
url = {http://eudml.org/doc/208034},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Mariusz Frąckiewicz
AU - Henryk Palus
TI - KHM clustering technique as a segmentation method for endoscopic colour images
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 1
SP - 203
EP - 209
AB - 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.
LA - eng
KW - biomedical colour image segmentation; k-harmonic means technique; k-means technique
UR - http://eudml.org/doc/208034
ER -

References

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  2. Borsotti, M., Campadelli, P. and Schettini, R. (1998). Quantitative evaluation of color image segmentation results, Pattern Recognition Letters 19(8): 741-747. Zbl0908.68204
  3. Cheng, H., Jiang, X., Sun, Y. and Wang, J. (2001). Color image segmentation: Advances and prospects, Pattern Recognition 34(12): 2259-2281. Zbl0991.68137
  4. 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. 
  5. 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. 
  6. Hamerly, G.J. (2003). Learning Structure and Concepts in Data through Data Clustering, Ph.D. thesis, University of California, San Diego, CA. 
  7. Linde, Y., Buzo, A. and Gray, R. (1980). An algorithm for vector quantizer design, IEEE Transactions on Communications 28(1): 84-95. 
  8. Lloyd, S. (1982). Least squares quantization in PCM, IEEE Transactions on Information Theory 28(2): 129-137. Zbl0504.94015
  9. 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. 
  10. 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. 
  11. Zhang, B. (2000). Generalized k-harmonic means-Boosting in unsupervised learning, Technical Report TR HPL-2000137, Hewlett Packard Labs, Palo Alto, CA. 
  12. 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. Zbl0987.68917

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  3. Kristian Sabo, Center-based l₁-clustering method
  4. Anna Fabijańska, Tomasz Węgliński, Krzysztof Zakrzewski, Emilia Nowosławska, Assessment of hydrocephalus in children based on digital image processing and analysis

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