An Evaluation of Image Compression Algorithms for Colour Retinal Images

  • Gerald Schaefer
  • Roman Starosolski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)


Diabetic retinopathy is the leading cause of blindness in the adult population. Mass-screening efforts, during which high resolution images of the retina are captured, are therefore underway in order to detect the disease in its early stages. In this paper we evaluate the compression performance of several lossless image compression algorithms that could be employed in a retina Picture Archiving and Communications System to lessen the demand on computing resources. The algorithms we analyse are TIFF PackBits, Lossless JPEG, JPEG-LS, and JPEG2000 all of which are incorporated in the current DICOM standard together with the non-standard CALIC algorithm for benchmark comparison. Compression performance is evaluated in terms of compression ratio, compression speed, and decompression speed. Based on a large dataset of more than 800 colour retinal images, divided into groups according to retinal region (nasal, posterior, and temporal) and image size, JPEG-LS is found to be the most suitable compression algorithm, offering good compression ratios combined with high compression and decompression speed. Compression ratios can be further improved through the application of a reversible colour space transformation prior to compression as a second set of experiments show.


retinopathy retinal images lossless image compression colour space transform DICOM PACS 


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  1. 1.
    Starosolski, R., Schaefer, G.: Lossless compression of color medical retinal images. In: 20th European Multiconference, pp. 79–84 (2006)Google Scholar
  2. 2.
    National Electrical Manufacturers Association: Digital Imaging and Communications in Medicine (DICOM). Standards Publication PS 3.1-2004 (2004)Google Scholar
  3. 3.
    Mildenberger, P., Eichelberg, M., Martin, E.: Introduction to the DICOM standard. European Radiology 12, 920–927 (2002)CrossRefGoogle Scholar
  4. 4.
    Adobe Systems Inc.: TIFF 6.0.1 specification (1995)Google Scholar
  5. 5.
    Langdon, G., Gulati, A., Seiler, E.: On the JPEG model for lossless image compression. In: 2nd Data Compression Conference, pp. 172–180 (1992)Google Scholar
  6. 6.
    ISO/IEC: Lossless and near-lossless compression of continuous-tone images - baseline. ISO/IEC International Standard 14495-1 (1999)Google Scholar
  7. 7.
    ISO/IEC: JPEG2000 image coding system: Core coding system. ISO/IEC International Standard 15444-1 (2002)Google Scholar
  8. 8.
    Wu, X., Memon, N.: Context-based adaptive lossless image codec. IEEE Trans. Communications 45(4), 437–444 (1997)CrossRefGoogle Scholar
  9. 9.
    Weinberger, M., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Processing 9(8), 1309–1324 (1996)CrossRefGoogle Scholar
  10. 10.
    Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: An overview. IEEE Trans. Consumer Electronics 46(4), 1103–1127 (2000)CrossRefGoogle Scholar
  11. 11.
    Wu, X.: Lossless compression of continuous-tone images via context selection, quantization, and modeling. IEEE Trans. Image Processing 6(5), 656–664 (1997)CrossRefGoogle Scholar
  12. 12.
    Mueller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int. Journal of Medical Informatics 73(1), 1–23 (2004)CrossRefGoogle Scholar
  13. 13.
    ISO/IEC: Lossless and near-lossless compression of continuous-tone images - extensions. ISO/IEC International Standard 14495-2 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gerald Schaefer
    • 1
  • Roman Starosolski
    • 2
  1. 1.School of Engineering and Applied ScienceAston UniversityU.K.
  2. 2.Institute of Computer ScienceSilesian University of TechnologyPoland

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