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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The actual blur may be caused by a different kernel and is a function of scanner modulation transfer function, (auto-)focus quality and method, and vary with location, due to specimen height variations.
References
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 Group
Henwood, A.: Microscopic quality control of haematoxylin and eosin – know your histology. Connection 14, 115–120 (2010). 6392 Via Real Carpinteria, CA 93013 USA: DAKO
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
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
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)
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)
Bertheau, P., Ameisen, D.: U.S. Patent Application 13/993,988 (2011)
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)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
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). ACM
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 1993
Lubin, J.: A visual discrimination model for imaging system design and evaluation. Vis. Models Target Detect. Recogn. 2, 245–357 (1995)
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)
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)
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)
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)
Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)
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)
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 2011
http://www.virtualpathology.leeds.ac.uk/slidelibrary/. Accessed Oct 2015
http://live.ece.utexas.edu/research/quality/vifp_release.zip. Accessed Oct 2015
Yagi, Y., Hashimoto, N.: Real Time Image Quality Assessment for WSI. Presentation at Pathology Visions, Boston, MA, October 2015
https://en.wikipedia.org/wiki/Box_blur/. Accessed Nov 2015
Acknowledgement
Ali Avanaki would like to thank Eddie Knippel for his comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Avanaki, A.R.N., Espig, K.S., Xthona, A., Lanciault, C., Kimpe, T.R.L. (2016). Automatic Image Quality Assessment for Digital Pathology. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-41546-8_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41545-1
Online ISBN: 978-3-319-41546-8
eBook Packages: Computer ScienceComputer Science (R0)