Skip to main content

Estimating the Detectability of Small Lesions in High Resolution MR Compressed Images

  • Conference paper
Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

Included in the following conference series:

Abstract

Quality degradation in high resolution magnetic resonance (MR) images compressed with JPEG2000 is studied. The present study combines the results of ten quantitative quality criteria with four mathematical observer models to estimate the maximum achievable compression rate (CR) that does not affect an effective diagnosis of specific pathologies related with small lesions detection such as Multiple Sclerosis or Virchow Robins disease. The graphical behavior of metrics employed is presented. At bitrate = 0.062 bpp (CR=160:1) the most compression is achieved while images still preserve the information needed for a safe diagnosis. Images compressed using smaller values than this would be no longer useful for diagnosis tasks. This result facilitates a safer use of the JPEG 2000 codec when compressing high resolution images like the images tested, and assures a safer diagnosis task when dealing with small size lesions that define certain pathology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Acharya, T., Ray, A.K.: Image processing Principles and applications, p. 385. John Wiley & Sons, Inc, Hoboken, ISBN-13 978-0-471-71998-4

    Google Scholar 

  2. Chen, H.H.: Quality Assessment of high spatial resolution for MRI, Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Google Scholar 

  3. Kalyanpur, M.A., et al.: Evaluation of JPEG and Wavelet Compression of Body CT Images for Direct Digital Teleradiologic Transmission. Radiology 217, 772–779 (2000)

    Google Scholar 

  4. Perlmutter, S.M., Cosman, P.C., Tseng, C.-W., Olshen, R.A., Gray, R.M., Li, K.C.P., Bergin, C.J.: Medical Image Compression and Vector Quantization. Statistical Science 13(1), 30–53 (1998)

    Article  MATH  Google Scholar 

  5. Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Signal Processing: Image Commun. 17, 3–48 (2002)

    Article  Google Scholar 

  6. Santa-Cruz, D., Grosbois, R., Ebrahimi, T.: JPEG 2000 performance evaluation and assessment. Signal Processing: Image Commun. 17, 113–130 (2002)

    Article  Google Scholar 

  7. Foes, D.H., et al.: JPEG2000 compression of medical imagery. In: Medical Imaging 2000: Pacs desing and Evaluation: Engineering and Clinical Issues, Image Compression and Presentation, San Diego, California, USA (2000)

    Google Scholar 

  8. DICOM protocol, http://www.xray.hmc.psu.edu/dicom/

  9. Barret, H.H., et al.: Model Observers for assessment of image quality. Colloquium Paper. Proc. Natl. Acad. Sci. USA 90, 9758–9765 (1993)

    Article  Google Scholar 

  10. Zhang, Y., Pham, B., Eckstein, M.P.: Evaluation of JPEG2000 encoder options: hu-man and model observer detection of variable signals in X-Ray coronary angiograms. IEEE Trans. On Med. Imaging 23(5) (May 2004)

    Google Scholar 

  11. Penedo, M., et al.: Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms. IEEE Trans. on Information Technology in Biomedicine 10(2) (April 2006)

    Google Scholar 

  12. Chawla, A.S.: Analyzing the effect of dose reduction on the detection of mammographic lesions using mathematical observer models. Med Phys. 34(8), 3385–3398 (2007)

    Article  MathSciNet  Google Scholar 

  13. Adams, M., Kossentini, F.: JasPer: a software-based JPEG2000 codec implementation. In: Proceedings of IEEE International Conference on Image Processing, vol. 2, pp. 53–56. Institute of Electrical and Electronics Engineers, Vancouver, British Columbia, Canada (2000)

    Google Scholar 

  14. JasPer (JPEG 2000 implementation on C), http://www.ece.unic.ca/mdadams/jasper

  15. Eskicioglu, A., et al.: Quality measurement for monochrome compressed images in the past 25years. In: Proc. of the International Conference on Acoustics Speech (ICASSP), pp. 1907–1910 (2000) ISBN: 0-7803-6293-4

    Google Scholar 

  16. Wang, Z., et al.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. on Image Processing 13(4) (April 2004)

    Google Scholar 

  17. Delgorge, C., et al.: Towards a New Tool for the Evaluation of the Quality of Ultrasound Compressed Images. IEEE Trans. on Medical Imaging 25(11) (November 2006)

    Google Scholar 

  18. Shnayderman, A., et al.: A multidimensional image quality measure using Singular Value Decomposition, Department of Computer and Information Science, CUNY Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210

    Google Scholar 

  19. Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. In: Proceedings of the International Conference on Image Processing, September 2002, pp. 477–480 (2002)

    Google Scholar 

  20. Burgess, E., Colborne, B.: Visual Signal Detection. IV Observer Inconsistency, Opt Soc. Am. A 5(4) (May 1988)

    Google Scholar 

  21. Eckstein, M.P., Abbey, C.K., Bochud, F.O.: Visual Signal Detection in structured backgrounds. IV Figures of Merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses. J. Opt. Soc. Am. A 17(2) (February 2000)

    Google Scholar 

  22. Zhang, Y., Pham, B., Eckstein, M.P.: Evaluation of internal noise methods for Hotel-ling observer models. Med. Phys. 34(8) (August 2007)

    Google Scholar 

  23. Chakraborty, D.P., Berbaum, K.S.: Observer studies involving detection and localization: Modeling, analysis, and validation. Med. Phys. 31(8) (August 2004)

    Google Scholar 

  24. Hanley, J.A., McNeil, B.J.: The Meaning and Use of the Area under a Receiver Operat-ing Characteristic (ROC) Curve. Radiology 143, 29–36 (1982)

    Google Scholar 

  25. López de Ullibarri, G.I., Píta, S.: Curvas ROC. Cad Aten Primaria 5(4), 229–235 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Aurélio Campilho Mohamed Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Paz, J., Pérez, M., Miranda, I., Schelkens, P. (2008). Estimating the Detectability of Small Lesions in High Resolution MR Compressed Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69812-8_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics