Automatic Perceptual Color Quantization of Dermoscopic Images

Vittoria Bruni, Giuliana Ramella, Domenico Vitulano


The paper presents a novel method for color quantization (CQ) of dermoscopic images. The proposed method consists of an iterative procedure that selects image regions in a hierarchical way, according to the visual importance of their colors. Each region provides a color for the palette which is used for quantization. The method is automatic, image dependent and computationally not demanding. Preliminary results show that the mean square error of quantized dermoscopic images is competitive with existing CQ approaches.


  1. Argenziano, G., Soyer, H., and Giorgi, V. D. (2002). Dermoscopy:a tutorial. EDRA Medical Publishing and New Media, +cd edition.
  2. Battiato, S., Mancuso, M., Bosco, A., and Guarnera, M. (2001). Psychovisual and statistical optimization of quantization tables for dct compression engines. In Proc. 11th Int. Conf. Image Analysis and Processing.
  3. Beghdadi, A., Larabi, M., Bouzerdoum, A., and K.M. Iftekharuddin, K. M. (2013). A survey of perceptual image processing methods. In Signal Processing: Image Communication, 28, 811-831.
  4. Braquelaire, J. and Brun, L. (1997). Comparison and optimization of methods of color image quantization. In IEEE Trans.on Image Processing, 6 1048-052.
  5. Braun, R. P., Rabinovitz, H., Oliviero, M., Kopf, A., and Saurat, J. (2005). Dermoscopy of pigmented skin lesions. In Journal of the American Academy of Dermatology, 52 (1), 109-121.
  6. Brun, L. and Trmeau, A. (2002). Digital color imaging handbook, chapter 9: Color quantization. In Electrical and Applied Signal Processing. CRC Press.
  7. Bruni, V., Crawford, A., Kokaram, A., and Vitulano, D. (2013). Semi-transparent blotches removal from sepia images exploiting visibility laws. In Signal Image and Video Processing, 7(1), 11-26.
  8. Bruni, V., Crawford, A., and Vitulano, D. (2006). Visibility based detection of complicated objects: a case study. In Proc. of IEE CVMP 06.
  9. Burger, W. and Burge, M. (2009). Principles of Digital Image Processing. Undergraduate Topics in Computer Science, Springer-Verlag.
  10. Celebi, M. (2009). An effective color quantization method based on the competitive learning paradigm. In Proc. of Int. Conf. on Image Proc., Computer Vision and Pattern Rec.
  11. Celebi, M., Wen, Q., Hwang, S., and Schaefer, G. (2013). Color quantization of dermoscopy images using the kmeans clustering algorithm. In Color Medical Image Analysis, 87-107. Celebi, M. E., Schaefer, G. Eds., Lecture Notes in Computational Vision and Biomechanics, 6, Springer.
  12. Celebi, M. E. (2011). Improving the performance of kmeans for color quantization. In Image and Vision Computing 29, 260-271.
  13. Celebi, M. E., Hwang, S., and Wen, Q. (2014). Color quantization using the adaptive distributing units algorithm. In Imaging Science Journal 62(2), 80-91.
  14. Cheng, S. and Yang, C. (2001). Fast and novel technique for color quantization using reduction of color space dimensionality. In Pattern Recognition Letters, 22(8):845-856. Elsevier.
  15. Frazor, R. and Geisler, W. (2006). Local luminance and contrast in natural in natural images, 46. In Vision Research.
  16. Gonzalez, R. C. and Woods, R. E. (2002). Digital Image Processing. Prentice Hall, 2nd edition.
  17. Heckbert, P. (1982). Color image quantization for frame buffer display. In Proc. ACM SIGGRAPH 7882 16(3), 297-307.
  18. Hruschka, E., Campello, R., Leon, A. F. F. P., and de Carvalho, A. (2009). A survey of evolutionary algorithms for clustering. In IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews VOL. 39, 2, pp. 133-155.
  19. Korotkov, K. and Garcia, R. (2012). Computerized analysis of pigmented skin lesions: A review. In Artificial Intelligence in Medicine, 56, 69-90.
  20. Kuriki, I. (2004). Testing the possibility of average-color perception from multi-colored patterns. In Optical Review, 11 (4), 249-257.
  21. Mallat, S. (1998). A Wavelet Tour of Signal Processing. Academic Press.
  22. Monte, V., Frazor, R., Bonin, V., Geisler, W., and Corandin, M. (2005). Independence of luminance and contrast in natural scenes and in the early visual system 8(12). In Nature Neuroscience.
  23. Moorthy, A. and Bovik, A. (2009). Visual importance pooling for image quality assessment. In IEEE Journal on Special Topics in Sig. Proc., 3(2).
  24. Palomo, E. and Domnguez, E. (2014). Hierarchical color quantization based on self-organization. In Journal of Mathematical Imaging and Vision, 49,1-19.
  25. Plataniotis, K. and Venetsanopoulos, N. (2000). Color image processing and applications. In Communications of the ACM, 34, 30-44.
  26. Ramella, G. and di Baja, G. S. (2013). A new technique for color quantization based on histogram analysis and clustering. In International Journal Pattern Recognition and Artificial Intelligence, 27 (3).
  27. Rosch, E. (1978). Cognition and categorization, principles of categorization. In Rosch, E., Lloyd, B.B. Ed., Erlbaum, Hillsdale.
  28. Schaefer, G. and Nolle, L. (2014). A hybrid color quantization algorithm incorporating a human visual perception model. In Computational Intelligence.
  29. Wallace, G. (1991). The jpeg still picture compression standard. In Communications of the ACM, 34, 30-44.
  30. Weeks, A. R. (1998). Fundamentals of electronic image processing. In SPIE-The International Society for Optical Engineering, Bellingham, Washington USA.
  31. Winkler, S. (2005). Digital Video Quality, Vision Models and Metrics. Wiley.
  32. Wu, X. (1991). Efficient statistical computations for optimal color quantization. In Graphics gems. Academic Press.

Paper Citation

in Harvard Style

Bruni V., Ramella G. and Vitulano D. (2015). Automatic Perceptual Color Quantization of Dermoscopic Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 323-330. DOI: 10.5220/0005304903230330

in Bibtex Style

author={Vittoria Bruni and Giuliana Ramella and Domenico Vitulano},
title={Automatic Perceptual Color Quantization of Dermoscopic Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},

in EndNote Style

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Automatic Perceptual Color Quantization of Dermoscopic Images
SN - 978-989-758-089-5
AU - Bruni V.
AU - Ramella G.
AU - Vitulano D.
PY - 2015
SP - 323
EP - 330
DO - 10.5220/0005304903230330