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A Quality Analysis on JPEG 2000 Compressed Leukocyte Images by Means of Segmentation Algorithms

  • Alexander Falcón-Ruiz
  • Juan Paz-Viera
  • Alberto Taboada-Crispí
  • Hichem Sahli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

Reducing image file size by means of lossy compression algorithms can lead to distortions inimage contentaffectingdetection of fine detail structures, either by human orautomated observation. In the case of microscopic images of blood cells, which usually occupy large amounts of disk space, the use of such procedures is justified within a controlled quality loss. Although JPEG 2000 remains as the accepted standard for lossycompression, still a set of guidelines need to be established in order to use this codec in its lossy mode and for particular applications. The present paper deals with a quality analysis of reconstructed microscopic leukocytes images after they have beenlossy compressed. The quality loss is investigated near the lower compression boundby evaluating the performance of several segmentation algorithms together with objective quality metrics. The value of compression rate of142:1 is estimated from the experiments.

Keywords

microscopicimages leukocytes segmentation JPEG 2000 compression 

References

  1. 1.
    Acharya, T., Ray, A.K.: Image Processing Principles and Applications. John Wiley & Sons, Inc., Hoboken (2005)CrossRefGoogle Scholar
  2. 2.
    Lau, C., et al.: Telemedicine. In: Kim, Y., Horri, S. (eds.) Handbook of Medical Imaging, vol. 3, pp. 305–331. SPIE, Bellingham (2000)Google Scholar
  3. 3.
    Clunie, D.A.: DICOM Supplement 61: JPEG 2000 Transfer Syntaxes (2002), ftp://medical.nema.org/medical/dicom/final/sup61_ft.pdf
  4. 4.
    Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. 1, Signal Processing: Image Communication 17, 3–48 (2002)Google Scholar
  5. 5.
    Foes, D.H., et al.: JPEG 2000 compression of medical imagery. In: Proc. SPIE, San Diego, California, vol. 3980 (2002)Google Scholar
  6. 6.
    Penedo, M., Lado, M.J., Tahoces, P.G., Souto, M., Vidal, J.J.: Effects of JPEG 2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms. IEEE Trans. on Information Technology in Biomedicine 10(2) (2006)Google Scholar
  7. 7.
    Zhang, Y., Pham, B., Eckstein, M.P.: Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in X-Ray coronary angiograms. IEEE Trans. On Med. Imaging 23(5) (2004)Google Scholar
  8. 8.
    Paz, J., Pérez, M., Schelkens, P., Rodríguez, J.: Impact of JPEG 2000 Compression on Lesion Detection in MR Imaging. Journal of Medical Physics 36(11), 4967–4976 (2009)CrossRefGoogle Scholar
  9. 9.
    Adams, M., Kossentini, F.: JasPer: a software based JPEG 2000 codec implementation. In: Proc. of IEEE International Conference on Image Processing, vol. 2, pp. 53–56. Institute of Electrical and Electronics Engineers, Vancouver, British Columbia, Canada (2002)Google Scholar
  10. 10.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. on Image Proc. 13(4) (2004)Google Scholar
  11. 11.
    Lee, J.K.T.: Interpretation accuracy and pertinence. American College of Radiology 4 (2002)Google Scholar
  12. 12.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)CrossRefGoogle Scholar
  13. 13.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  14. 14.
    Gupta, L., Sortrakul, T.: A gaussian-mixture-based image segmentation algorithm. Pattern Recognition 31(3), 315–325 (1998)CrossRefGoogle Scholar
  15. 15.
    Huttenlocher, D., Klanderman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)CrossRefGoogle Scholar
  16. 16.
    Cohen, L., Vinet, L., Sander, P.T., Gagalowicz, A.: Hierarchical Regional Based Stereo Matching. In: Proc. Computer Vision and Pattern Recognition, pp. 416–421 (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Falcón-Ruiz
    • 1
  • Juan Paz-Viera
    • 1
  • Alberto Taboada-Crispí
    • 1
  • Hichem Sahli
    • 2
  1. 1.Center for Studies on Electronics and Information TechnologiesUniversidad Central de Las VillasSanta ClaraCuba
  2. 2.Dept. Electronics & Informatics, VUB-ETROVrijeUniversiteitBrusselBrusselsBelgium

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