Research article Special Issues

Objective and Subjective Assessment of Digital Pathology Image Quality

  • Received: 01 January 2015 Accepted: 12 March 2015 Published: 25 March 2015
  • The quality of an image produced by the Whole Slide Imaging (WSI) scanners is of critical importance for using the image in clinical diagnosis. Therefore, it is very important to monitor and ensure the quality of images. Since subjective image quality assessments by pathologists are very time-consuming, expensive and difficult to reproduce, we propose a method for objective assessment based on clinically relevant and perceptual image parameters: sharpness, contrast, brightness, uniform illumination and color separation; derived from a survey of pathologists. We developed techniques to quantify the parameters based on content-dependent absolute pixel performance and to manipulate the parameters in a predefined range resulting in images with content-independent relative quality measures. The method does not require a prior reference model. A subjective assessment of the image quality is performed involving 69 pathologists and 372 images (including 12 optimal quality images and their distorted versions per parameter at 6 different levels). To address the inter-reader variability, a representative rating is determined as a one-tailed 95% confidence interval of the mean rating. The results of the subjective assessment support the validity of the proposed objective image quality assessment method to model the readers’ perception of image quality. The subjective assessment also provides thresholds for determining the acceptable level of objective quality per parameter. The images for both the subjective and objective quality assessment are based on the HercepTestTM slides scanned by the Philips Ultra Fast Scanners, developed at Philips Digital Pathology Solutions. However, the method is applicable also to other types of slides and scanners.

    Citation: Prarthana Shrestha, Rik Kneepkens, Gijs van Elswijk, Jeroen Vrijnsen, Roxana Ion, Dirk Verhagen, Esther Abels, Dirk Vossen, and Bas Hulsken. Objective and Subjective Assessment of Digital Pathology Image Quality[J]. AIMS Medical Science, 2015, 2(1): 65-78. doi: 10.3934/medsci.2015.1.65

    Related Papers:

  • The quality of an image produced by the Whole Slide Imaging (WSI) scanners is of critical importance for using the image in clinical diagnosis. Therefore, it is very important to monitor and ensure the quality of images. Since subjective image quality assessments by pathologists are very time-consuming, expensive and difficult to reproduce, we propose a method for objective assessment based on clinically relevant and perceptual image parameters: sharpness, contrast, brightness, uniform illumination and color separation; derived from a survey of pathologists. We developed techniques to quantify the parameters based on content-dependent absolute pixel performance and to manipulate the parameters in a predefined range resulting in images with content-independent relative quality measures. The method does not require a prior reference model. A subjective assessment of the image quality is performed involving 69 pathologists and 372 images (including 12 optimal quality images and their distorted versions per parameter at 6 different levels). To address the inter-reader variability, a representative rating is determined as a one-tailed 95% confidence interval of the mean rating. The results of the subjective assessment support the validity of the proposed objective image quality assessment method to model the readers’ perception of image quality. The subjective assessment also provides thresholds for determining the acceptable level of objective quality per parameter. The images for both the subjective and objective quality assessment are based on the HercepTestTM slides scanned by the Philips Ultra Fast Scanners, developed at Philips Digital Pathology Solutions. However, the method is applicable also to other types of slides and scanners.


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    [1] Pantanowitz L (2010) Digital images and the future of digital pathology. J Pathol Inform 1: 15. doi: 10.4103/2153-3539.68332
    [2] Krupinski EA, Silverstein LD, Hashmi SF, et al. (2012) Observer performance using virtual pathology slides: Impact of LCD color reproduction accuracy. J Digit Imaging 25: 738-743. doi: 10.1007/s10278-012-9479-1
    [3] Redondo R, Bueno G, Cristbal G, et al. (2012). Quality evaluation of microscopy and scanned histological images for diagnostic purposes. Micron 43: 334-343. doi: 10.1016/j.micron.2011.09.010
    [4] Murakami Y, Gunji H, Kimura F, et al. (2012) Color correction in whole slide digital pathology. Twentieth Color and Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications. pp. 253-258.
    [5] Korzyska A, Neuman U, Lopez C, et al. (2010) The method of immunohistochemical images standardization. Image Processing and Communications Challenges 2, Advances in Intelligent and Soft Computing. 84: 213-221. doi: 10.1007/978-3-642-16295-4_24
    [6] Hashimoto N, Ohyama N, Bautista PA, et al. (2012) Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 3: 9. doi: 10.4103/2153-3539.93891
    [7] Kayser K, Grtler J, Metze K, et al. (2008) How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology). Diagn Pathol 3 (Suppl 1): S11.
    [8] Duthaler S, Sun Y, Nelson BJ (2005) Autofocusing algorithm selection in computer microscopy. Intelligent Robots and Systems, 2005. 2005 IEEE/RSJ International Conference on IEEE:419-425.
    [9] Dako HercepTestTM Product information. Available from: http://www.dako.com/28633_04m ay10_herceptest__brochure-9086.pdf.
    [10] Vossen D, Mueller D,  Hulsken B, et al.(2011) Real-time deformable registration of multi-modal whole slides for digital pathology. Comput Med Imaging Graph 35: 542-556.
    [11] IEC 61966-2-1: 1999 Color management default RGB colour space - sRGB.
    [12] ITU-R BT.601: Definition for RGB to YCbCr color space conversion.
    [13] Yeo TTE, Ong SH, Jayasooriah and Sinniah R, et al. (1993) Autofocusing for tissue microscopy. Image Vision Comput 11: 629-639. doi: 10.1016/0262-8856(93)90059-P
    [14] Thompson W, Fleming R, Creem-Regehr S and Stefanucci JK, et al. (2011) Visual perception from a computer graphics perspective. A. K. Peters Ltd..
    [15] Tzeng D (1999) Spectral-based color separation algorithm development for multiple-ink color reproduction. Ph. D. the- sis, R.I.T., Rochester, NY.
    [16] Javed O, Sha_que K and Shah M (2002) A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information. IEEE Workshop on Motion and Video Computing.
    [17] Presentation ® (2014) Available from: http://www.neurobs.com/menu_presentation/menu_fea tures/features_overview.
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  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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