Advertisement

Automatic Image Quality Assessment for Digital Pathology

  • Ali R. N. Avanaki
  • Kathryn S. Espig
  • Albert Xthona
  • Christian Lanciault
  • Tom R. L. Kimpe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Slide quality is an important factor in pathology workflow and diagnosis. We examine the extent of quality variations in digitized hematoxylin-eosin (H&E) slides due to variations and errors in staining and/or scanning (e.g., out-of-focus blur & stitching). We propose two automatic quality estimators by adapting image quality assessment (IQA) methods that are originally developed for natural images. For the first estimator, we assume a gold-standard reference digital pathology slide is available. Quality of a given slide is estimated by comparing the slide to such a reference using a full-reference perceptual IQA method such as VIF (visual information fidelity) or SSIM (structural similarity metric). Our second estimator is based on IL-NIQE (integrated local natural image quality evaluator), a no-reference IQA, which we train using a set of artifact-free H&E high-power images (20× or 40×) from breast tissue. The first estimator (referenced) predicts marked quality reduction of images with simulated blurring as compared to the artifact-free originals used as references. The histograms of scores by the second estimator (no-reference) for images with artifact (blur, stitching, folded tissue, or air bubble artifacts) and for artifact-free images are highly separable. Moreover, the scores by the second estimator are correlated with the ratings given by a pathologist. We conclude that our approach is promising and further research is outlined for developing robust automatic quality estimators.

Keywords

Whole slide imaging (WSI) 

Notes

Acknowledgement

Ali Avanaki would like to thank Eddie Knippel for his comments.

References

  1. 1.
    Barr, T., Nicol, K., Billiter, D., Wohlever, K., Baker, P., Prasad, V.: Utility of VIPER (virtual imaging for pathology, education and research) in continuing medical education and slide surveys. Lab. Invest. 89, 298A–298A (2009). 75 Varick St, 9th Flr, New York, NY 10013-1917 USA: Nature Publishing GroupCrossRefGoogle Scholar
  2. 2.
    Henwood, A.: Microscopic quality control of haematoxylin and eosin – know your histology. Connection 14, 115–120 (2010). 6392 Via Real Carpinteria, CA 93013 USA: DAKOGoogle Scholar
  3. 3.
    Brown, S.: The Science and Application of Hematoxylin and Eosin Staining. http://mhpl.facilities.northwestern.edu/files/2013/10/The-Science-and-Application-of-Hematoxylin-and-Eosin-Staining-6-5-2012.pdf. Accessed 21 Oct 2015
  4. 4.
    Anderson, N., Badano, A.: Technical Performance Assessment of Digital Pathology Whole Slide Imaging Devices, Draft Guidance for Industry and FDA Staff. http://www.fda.gov/ucm/groups/fdagov-public/@fdagov-meddev-gen/documents/document/ucm435355.pdf. Accessed 21 Oct 2015
  5. 5.
    Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: whole-slide imaging and beyond. Annu. Rev. Pathol. Mech. Dis. 8, 331–359 (2013)CrossRefGoogle Scholar
  6. 6.
    Ameisen, D., Deroulers, C., Perrier, V., Bouhidel, F., Battistella, M., Legrès, L., Janin, A., Bertheau, P., Yunès, J.B.: Towards better digital pathology workflows: programming libraries for high-speed sharpness assessment of Whole Slide Images. Diagn. Pathol. 9(Suppl 1), S3 (2014)CrossRefGoogle Scholar
  7. 7.
    Bertheau, P., Ameisen, D.: U.S. Patent Application 13/993,988 (2011)Google Scholar
  8. 8.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  9. 9.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  10. 10.
    Mantiuk, R., Kim, K.J., Rempel, A.G., Heidrich, W.: HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graph. (TOG) 30(4), 40 (2011). ACMCrossRefGoogle Scholar
  11. 11.
    Lubin, J.: The use of psychophysical data and models in the analysis of display system performance. In: Digital Images and Human Vision, pp. 163–178. MIT Press, Cambridge, October 1993Google Scholar
  12. 12.
    Lubin, J.: A visual discrimination model for imaging system design and evaluation. Vis. Models Target Detect. Recogn. 2, 245–357 (1995)CrossRefGoogle Scholar
  13. 13.
    Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimedia 17(1), 50–63 (2015)CrossRefGoogle Scholar
  14. 14.
    Gu, K., Zhai, G., Lin, W., Yang, X., Zhang, W.: No-reference image sharpness assessment in autoregressive parameter space. IEEE Trans. Image Process. 24(10), 3218–3231 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, Y., Wang, J., Cho, S., Finkelstein, A., Rusinkiewicz, S.: A no-reference metric for evaluating the quality of motion deblurring. ACM Trans. Graph. 32(6), 175 (2013)Google Scholar
  16. 16.
    Xue, W., Mou, X., Zhang, L., Bovik, A.C., Feng, X.: Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Trans. Image Process. 23(11), 4850–4862 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar
  19. 19.
    Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ye, P., Doermann, D.: No-reference image quality assessment based on visual codebook. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3089–3092. IEEE, September 2011Google Scholar
  21. 21.
  22. 22.
  23. 23.
    Yagi, Y., Hashimoto, N.: Real Time Image Quality Assessment for WSI. Presentation at Pathology Visions, Boston, MA, October 2015Google Scholar
  24. 24.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ali R. N. Avanaki
    • 1
  • Kathryn S. Espig
    • 1
  • Albert Xthona
    • 1
  • Christian Lanciault
    • 3
  • Tom R. L. Kimpe
    • 2
  1. 1.Barco HealthcareBeavertonUSA
  2. 2.Healthcare DivisionBarco N.V.KortrijkBelgium
  3. 3.Department of PathologyOregon Health & Science UniversityPortlandUSA

Personalised recommendations