Skip to main content
SpringerLink
Log in

Quantification of image quality using information theory

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Aims of present study were to examine usefulness of information theory in visual assessment of image quality. We applied first order approximation of the Shannon’s information theory to compute information losses (IL). Images of a contrast-detail mammography (CDMAM) phantom were acquired with computed radiographies for various radiation doses. Information content was defined as the entropy Σp i log(1/p i ), in which detection probabilities p i were calculated from distribution of detection rate of the CDMAM. IL was defined as the difference between information content and information obtained. IL decreased with increases in the disk diameters (P < 0.0001, ANOVA) and in the radiation doses (P < 0.002, F-test). Sums of IL, which we call total information losses (TIL), were closely correlated with the image quality figures (r = 0.985). TIL was dependent on the distribution of image reading ability of each examinee, even when average reading ratio was the same in the group. TIL was shown to be sensitive to the observers’ distribution of image readings and was expected to improve the evaluation of image quality.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Pisano ED, Yaffe MJ (2005) Digital mammography. Radiology 234(2):353–362

    Article  PubMed  Google Scholar 

  2. Arakawa S, Itoh W, Kohda K, Suzuki T (1999) Novel computed radiography system with improved image quality by detection of emissions from both sides of an imaging plate. SPIE Proc 3659:572–581

    Article  Google Scholar 

  3. Arakawa S, Yasuda H, Kohda K, Suzuki T (2000) Improvement of image quality in CR mammography by detection of emissions from dual sides of an imaging plate. SPIE Proc 3977:590–600

    Article  Google Scholar 

  4. Fetterly KA, Schueler BA (2003) Performance evaluation of a “dual-side read” dedicated mammography computed radiography system. Med Phys 30(7):1843–1854

    Article  PubMed  Google Scholar 

  5. Ideguchi T, Higashida Y, Kawaji Y, Sasaki M, Zaizen M, Shibayama R et al (2004) New CR system with pixel size of 50 micron for digital mammography: physical imaging properties and detection of subtle microcalcifications. Radiat Med 22(4):218–224

    PubMed  Google Scholar 

  6. Skaane P, Young K, Skjennald A (2003) Population-based mammography screening: comparison of screen-film and full-field digital mammography with soft-copy reading: Oslo I study. Radiology 229(3):877–884

    Article  PubMed  Google Scholar 

  7. Skaane P, Hofvind S, Skjennald A (2007) Randomized trial of screen-film versus full-field digital mammography with soft-copy reading in population-based screening program: follow-up and final results of Oslo II study. Radiology 244(3):708–717

    Article  PubMed  Google Scholar 

  8. Samei E, Flynn MJ, Eyler WR (1999) Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. Radiology 213(3):727–734

    PubMed  CAS  Google Scholar 

  9. Engen RV, Young K, Bosmans H, Thijssen MA (2003) Addendum on digital mammography to Chapt. 3 of the European guidelines for quality assurance in mammography screening, version 1.0. EUREF

  10. Thijssen MAO, Thijssen HOM, Merx JL, Lindeijer JM, Bijkerk KR (1989) A definition of image quality: the image quality figure. In: Optimization of image quality and patient exposure in diagnostic radiology. BIR Report 20. British Institute of Radiology, London, pp 29–34

  11. Bijkerk KR, Thijssen MAO, Arnoldussen ThJM (2002) Manual CDMAM-phantom type 3.4., Department of Radiology University Medical Center Nijmegen St Radboud, The Netherlands

  12. Metz CE, Goodenough DJ, Rossmann K (1973) Evaluation of receiver operating characteristic curve data in terms of information theory, with applications in radiography. Radiology 109(2):297–303

    PubMed  CAS  Google Scholar 

  13. Niimi T, Imai K, Maeda H, Ikeda M (2007) Information loss in visual assessments of medical images. Eur J Radial 61(2):362–366

    Article  Google Scholar 

  14. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Google Scholar 

  15. Shannon CE, Weaver W (1949) The mathematical theory of communication. The University of Illinois Press, Urbana

    Google Scholar 

  16. American College of Radiology Committee on Quality Assurance in Mammography (1999) Mammography quality control manual, medical physicist’s section. Reston ACR, Reston

  17. Sobol WT, Wu X (1997) Parameterization of mammography normalized average glandular dose tables. Med Phys 24(4):547–554

    Article  PubMed  CAS  Google Scholar 

  18. Bijkerk KR, Thijssen MAO, Arnoldussen TJM (2002) Manual CDMAM-phantom type 3.4. Department of Radiology University Medical Center, Nijmegen, The Netherlands

  19. Hanley JA, MacGibbon B (2006) Creating non-parametric bootstrap samples using poisson frequencies. Comput Methods Prog Biomed 83(1):57–62

    Article  Google Scholar 

  20. Fastback F, Ricke J, Freund T, Werk M, Spors B, Baumann C, Perch M, Felix R (2002) Flat-panel digital radiography compared with storage phosphor computed radiography: assessment of dose versus image quality in phantom studies. Invest Radiol 37(11):609–614

    Article  Google Scholar 

  21. Feinstein AR (1985) The ‘Chagrin factor’ and qualitative decision analysis. Arch Intern Med 145:1257–1259

    Article  PubMed  CAS  Google Scholar 

  22. Tempany CM, Zou KH, Silverman SG, Brown DL, Kurtz AB, McNeil BJ (2000) Staging of advanced ovarian cancer: comparison of imaging modalities: report from the radiological diagnostic oncology group. Radiology 215(3):761–767

    PubMed  CAS  Google Scholar 

  23. Uffman M, Prokop M, Eisenhuber E, Fuchsjager M, Weber M, Schaefer-Prokop C (2005) Computed radiography and direct radiography: influence of acquisition dose on the detection of simulated lung legions. Invest Radiol 40(5):249–256

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiological Technology, Nagoya University School of Health Sciences, 1-1-20 Daiko-minami, Higashi-ku, Nagoya, 461-8673, Japan

    Takanaga Niimi, Hisatoshi Maeda, Mitsuru Ikeda & Kuniharu Imai

Authors
  1. Takanaga Niimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hisatoshi Maeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitsuru Ikeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kuniharu Imai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takanaga Niimi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Niimi, T., Maeda, H., Ikeda, M. et al. Quantification of image quality using information theory. Australas Phys Eng Sci Med 34, 481–488 (2011). https://doi.org/10.1007/s13246-011-0108-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-011-0108-y

Keywords

Search

Navigation