Advertisement

vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging

  • Claes LundströmEmail author
Original Article
  • 180 Downloads

Abstract

Purpose Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data.

Methods We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios.

Results The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable.

Conclusions vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.

Keywords

Image fidelity Image quality  Metric Diagnostic imaging vPSNR Data reduction  Image compression Visualization Perception 

Notes

Acknowledgments

This work was supported by the Swedish Foundation for Strategic Research, grant SM10-0022. The author would like to thank the participating radiologists and acknowledge the Center for Medical Image Science and Visualization, Linköping University, for access to leading edge infrastructure and image data. Image data used in this research were also obtained from The Cancer Imaging Archive (http://cancerimagingarchive.net/) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH. The author acknowledges the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC-IDRI Database used in this study.

Conflict of interest

Claes Lundström is also employed by Sectra AB, Sweden.

References

  1. 1.
    Andriole K, Wolfe J, Khorasani R, Treves S, Getty D, Jacobson F, Steigner M, Pan J, Sitek A, Seltzer S (2011) Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day. Radiology 259(2): 346–362Google Scholar
  2. 2.
    ESR expert panel on Image Compression (2011) Usability of irreversible image compression in radiological imaging. A Position Pap Eur Soc Radiol (ESR) Insights Imaging 2(2):103–115Google Scholar
  3. 3.
    Wang Z (2011) Applications of objective image quality assessment methods. IEEE Signal Process Mag (November) 28(6):137–142Google Scholar
  4. 4.
    Wang Z, Bovik AC (2009) Mean squared error : love it or leave it? IEEE Signal Process Mag (January) 26(1):98–117Google Scholar
  5. 5.
    Lubin J (1995) A visual discrimination model for imaging system design and evaluation. In: Menendez AR, Peli E (eds) Vision models for target detection and Recognition. World Scientific, Singapore, pp 245–283Google Scholar
  6. 6.
    Carnec M, Callet PL (2008) Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process Image Commun 4(April):239–256CrossRefGoogle Scholar
  7. 7.
    Daly S (1993) The visible differences predictor: an algorithm for the assessment of image fidelity. In: Watson AB (ed) Digital images and human vision. MIT press, Cambridge, pp 179–206Google Scholar
  8. 8.
    Mantiuk R, Daly S, Myszkowski K (2005) Predicting visible differences in high dynamic range images: model and its calibration. In: Proceedings of SPIE, pp 204–219Google Scholar
  9. 9.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612PubMedCrossRefGoogle Scholar
  10. 10.
    Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. Signals Syst Comput, pp 9–13Google Scholar
  11. 11.
    Sheikh H, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444PubMedCrossRefGoogle Scholar
  12. 12.
    Chandler DM, Hemami SS (2007) VSNR: a waveletbased visual signal-to-noise ratio for natural images. IEEE Trans Image Process 16(9):2284–2298PubMedCrossRefGoogle Scholar
  13. 13.
    Seghir Z, Hachouf F (2011) Distance transform measure based on edge-region information. An algorithm for image quality assessment. In: Third world congress on nature and biologically inspired computing, IEEE publishing, New York, pp 125–130Google Scholar
  14. 14.
    Moorthy A (2009) Visual importance pooling for image quality assessment. IEEE J Sel Top Signal Process 3(2):193–201CrossRefGoogle Scholar
  15. 15.
    Engelke U, Zepernick HJ (2010) Framework for optimal region of interestbased quality assessment in wireless imaging. J Electron Imaging 19(1):011005Google Scholar
  16. 16.
    Navas KA, Gayathri DKG, Athulya MS, Vasudev A (2011) MWPSNR: a new image fidelity metric. IEEE Recent Adv Intell Comput Syst 2011:627–632Google Scholar
  17. 17.
    Kusuma T, Zepernick HJ (2003) A reduced-reference perceptual quality metric for in-service image quality assessment. In: SympoTIC’03. Joint 1st workshop on mobile future and symposium on trends in communications, pp 71–74Google Scholar
  18. 18.
    Wang Z, Wu G, Sheikh H (2006) Quality-aware images. IEEE Trans Image Process 15(6):1680–1689Google Scholar
  19. 19.
    Lv X, Wang ZJ (2009) Reduced-reference image quality assessment based on perceptual image hashing. In: ICIP, pp 4361–4364Google Scholar
  20. 20.
    The adoption of lossy image data compression for the purpose of clinical interpretation. Technical report. The Royal College of Radiologists Board of the Faculty of, Clinical Radiology (2008)Google Scholar
  21. 21.
    Loose R, Braunschweig R, Kotter E, Mildenberger P, Simmler R, Wucherer M (2009) Compression of digital images in radiology results of a consensus conference. RöFo: Fortschritte auf dem Gebiete der Röntgenstrahlen und der Nuklearmedizin 181(1):32–37PubMedCrossRefGoogle Scholar
  22. 22.
    Koff D, Bak P, Brownrigg P, Hosseinzadeh D, Khademi A, Kiss A, Lepanto L, Michalak T, Shulman H, Volkening A (2009) Pan-Canadian evaluation of irreversible compression ratios (“lossy” compression) for development of national guidelines. J Digit Imaging 22(6):569–578PubMedCrossRefGoogle Scholar
  23. 23.
    Erickson BJ (2002) Irreversible compression of medical images. J Digit Imaging 15(1):5–14Google Scholar
  24. 24.
    Lee H, Lee KH, Kim KJ, Park S, Seo J, Shin YG, Kim B (2010) Advantage in image fidelity and additional computing time of JPEG2000 3D in comparison to JPEG2000 in compressing abdomen CT image datasets of different section thicknesses. Med Phys 37(8):4238–4248Google Scholar
  25. 25.
    Kim B, Lee KH, Kim KJ, Mantiuk R, Bajpai V, Kim TJ, Kim YH, Yoon CJ, Hahn S (2008) Prediction of perceptible artifacts in JPEG2000 compressed abdomen CT images using a perceptual image quality metric. Acad Radiol 15(3):314–325PubMedCrossRefGoogle Scholar
  26. 26.
    Kim KJ, Kim B, Lee KH, Mantiuk R, Kang HS, Seo J, Kim SY, Kim YH (2009) Objective index of image fidelity for JPEG2000 compressed body CT images. Med Phys 36(7):3218–3226Google Scholar
  27. 27.
    Kim KJ, Kim B, Mantiuk R, Richter T, Lee H, Kang HS, Seo J, Lee KH (2010) A comparison of three image fidelity metrics of different computational principles for JPEG2000 compressed abdomen CT images. IEEE Trans Med Imaging 29(8):1496–1503PubMedCrossRefGoogle Scholar
  28. 28.
    Pambrun J, Noumeir R (2011) Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses. In: IEEE International conference on multimedia and expo (ICME), IEEE publishing, New York, pp 1–6Google Scholar
  29. 29.
    Siegel E (2005) Compression of multislice CT: 2D vs. 3D JPEG2000 and effects of slice thickness. Proc SPIE 5748:162–170 CrossRefGoogle Scholar
  30. 30.
    Prokop M, Galanski M, Schaefer-Prokop C, van der Molen AJ (2003) Spiral and multislice computed tomography of the body (Thieme)Google Scholar
  31. 31.
    Preim B, Bartz D (2007) Visualization in medicine. Theory, algorithms, and applications. Series in Computer Graphics, Morgan KaufmannGoogle Scholar
  32. 32.
    Robertson AR (1990) Historical development of CIE recommended color difference equations. Color Res Appl 15(3):167–170Google Scholar
  33. 33.
    Poynton C (1997) Frequently asked questions about color, URL: http://www.poynton.com/PDFs/ColorFAQ.pdf. Acquired Aug 2012
  34. 34.
    Stokes M, Anderson M, Chandrasekar S, Motta R (1996) A standard default color space for the internet - sRGB, Version 1.10, URL: http://www.w3.org/Graphics/Color/sRGB. Acquired Aug 2012
  35. 35.
    Ljung P, Lundström C, Ynnerman A, Museth K (2004) Transfer function based adaptive decompression for volume rendering of large medical data sets. In: IEEE symposium on volume visualization and graphics, IEEE publishing, New York, pp 1–6 2004:25–32Google Scholar
  36. 36.
  37. 37.
    Kolen MJ, Brennan RL (2004) Test equating, scaling, and linking: methods and practices. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© CARS 2012

Authors and Affiliations

  1. 1.CMIV, Linköping University HospitalLinköpingSweden

Personalised recommendations