Normal Patch Retinex robust algorithm for white balancing in digital microscopy

Izabella Antoniuk; Artur Krupa; Radosław Roszczyk

doi:10.22630/MGV.2020.29.1.5

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Published:

Dec 21, 2020

Issue

Vol. 29 No. 1/4 (2020)

Section

Original research papers

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Izabella Antoniuk

Institute of Information Technology, Warsaw University of Life Sciences -- SGGW, Warsaw, Poland

https://orcid.org/0000-0003-3157-3381

Artur Krupa

Institute of Information Technology, Warsaw University of Life Sciences -- SGGW, Warsaw, Poland

https://orcid.org/0000-0001-8583-9873

Radosław Roszczyk

Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland

https://orcid.org/0000-0003-0721-6739

DOI: https://doi.org/10.22630/MGV.2020.29.1.5

Keywords : auto white balance algorithm, microscope image processing, staining of microscopic slides, digital microscopy

Abstract

The acquisition of accurately coloured, balanced images in an optical microscope can be a challenge even for experienced microscope operators. This article presents an entirely automatic mechanism for balancing the white level that allows the correction of the microscopic colour images adequately. The results of the algorithm have been confirmed experimentally on a set of two hundred microscopic images. The images contained scans of three microscopic specimens commonly used in pathomorphology. Also, the results achieved were compared with other commonly used white balance algorithms in digital photography. The algorithm applied in this work is more effective than the classical algorithms used in colour photography for microscopic images stained with hematoxylin-phloxine-saffron and for immunohistochemical staining images.

How to Cite

Antoniuk, I., Krupa, A., & Roszczyk, R. (2020). Normal Patch Retinex robust algorithm for white balancing in digital microscopy. Machine Graphics and Vision, 29(1/4), 79–94. https://doi.org/10.22630/MGV.2020.29.1.5

References

P. Baldevbhai. Color Image Segmentation for Medical Images using L*a*b* Color Space. IOSR Journal of Electronics and Communication Engineering, 1(2):24–45, 2012. https://doi.org/10.9790/2834-0122445. (Crossref)

M. Bertalmío, V. Caselles, and E. Provenzi. Issues about Retinex theory and contrast enhancement. International Journal of Computer Vision, 83(1), Jun 2009. https://doi.org/10.1007/s11263-009-0221-5. (Crossref)

P. Biecek. Perception of colours (in Polish). In Odkrywać! Ujawniać! Objaśniać! Zbiór esejów o sztuce prezentowania danych, pages 67–84. Fundacja Naukowa SmarterPoland.pl, 2016. http://biecek.pl/Eseje/indexKolory.html.

R. Davis. A correlated color temperature for illuminants. Bureau of Standards Journal of Research, 7(4):659, 1931. https://doi.org/10.6028/jres.007.039. (Crossref)

Eastman Kodak Company. KODAK Gray Card / R-27, 2020. https://www.kodak.com/en/motion/page/gray-cards. [Accessed Oct 2020].

H. Garud, A. Ray, M. Mahadevappa, et al. A fast auto white balance scheme for digital pathology. 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014, pages 153–156, 2014. https://doi.org/10.1109/BHI.2014.6864327. (Crossref)

B. Gilbert et al. MIRAX (MRXS). In Goode et al. [9]. [Accessed Oct 2020]. http://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/.

A. Goode, B. Gilbert, J. Harkes, et al. OpenSlide: A vendor-neutral software foundation for digital pathology. Journal of Pathology Informatics, 4(1):27, 2013. https://doi.org/10.4103/2153-3539.119005. (Crossref)

A. Goode, B. Gilbert, J. Harkes, et al., editors. OpenSlide, 2020. [Accessed Oct 2020]. https://openslide.org/.

K. Hasna Panikkaveettil and M. V. Beena. A survey on color normalization approach to histopathology images. International Journal of Advanced Engineering Research and Science, 3(4):103–105, 2016. https://www.neliti.com/publications/258867/.

M. Holek. File:Color temperature.svg. In Temperatura barwowa – Wikipedia, wolna encyklopedia [26]. From Wikimedia Commons. License: creative commons cc-by-sa 2.5 Poland [Accessed Oct 2020]. https://commons.wikimedia.org/wiki/File:Color_temperature.svg.

D. J. Jobson, Z. Rahman, and G. A. Woodell. Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 6(3):451–462, 1997. https://doi.org/10.1109/83.557356. (Crossref)

E. Y. Lam and G. S. K. Fung. Automatic white balancing in digital photography. In Lukac R., editor, Single-Sensor Imaging: Methods and Applications for Digital Cameras, pages 267–294. CRC Press, Boca Raton, 2017. https://doi.org/10.1201/9781315219363. (Crossref)

E. Land. The retinex theory of color vision. Scientific American, 237(6):108–128, Dec 1977. https://doi.org/10.1038/scientificamerican1277-108. (Crossref)

E. Land and J. McCann. Lightness and retinex theory. Journal of the Optical Society of America, 61(1):1–11, Jan 1971. https://doi.org/10.1364/JOSA.61.000001. (Crossref)

E. H. Land. The Retinex. American Scientist, 52(2):247–264. http://www.jstor.org/stable/27838994.

M. Macenko, M. Niethammer, and J.and others Marron. A method for normalizing histology slides for quantitative analysis. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pages 1107–1110, 2009. https://doi.org/10.1109/ISBI.2009.5193250. (Crossref)

Inductiveload (Wikimedia user). A diagram of the EM spectrum, showing the type, wavelength (with examples), frequency, the black body emission temperature. Adapted from em_spectrum3-new.jpg, which is a NASA image, Oct 2007. https://commons.wikimedia.org/wiki/File:EM_Spectrum_Properties.svg. From Wikimedia Commons. License: Attribution-ShareAlike 3.0 Unported cc-by-sa 3.0 [Accessed Jul 2020].

J. McCann. Color sensations and color perceptions. In Proc. 24th Asilomar Conf. Signals, Systems and Computers ACSSC, volume 1, pages 408–412, Pacific Grove, CA, USA, 5-7 Nov 1990. IEEE Computer Society. https://doi.org/10.1109/ACSSC.1990.523369. (Crossref)

A. B. Petro, C. Sbert, and J.-M. Morel. Multiscale retinex. Image Processing On Line, pages 71–88, 2014. https://doi.org/10.5201/ipol.2014.107. (Crossref)

E. Provenzi, L. De Carli, A. Rizzi, and D. Marini. Mathematical definition and analysis of the Retinex algorithm. Journal of the Optical Society of America A, 22(12):2613–2621, Dec 2005. https://doi.org/10.1364/JOSAA.22.002613. (Crossref)

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi. A spatially variant White-Patch and Gray-World method for color image enhancement driven by local contrast. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(10):1757–1770, 2008. https://doi.org/10.1109/TPAMI.2007.70827. (Crossref)

Z. Rahman, D. J. Jobson, and G. A. Woodell. Multi-scale retinex for color image enhancement. In Proc. 3rd IEEE Int. Conf. Image Processing, volume 3, pages 1003–1006, Sep 19, 1996. https://doi.org/10.1109/ICIP.1996.560995. (Crossref)

M. Saha, S. Agarwal, I. Arun, et al. Histogram based thresholding for automated nucleus segmentation using breast imprint cytology. In S. Gupta, S. Bag, K. Ganguly, et al., editors, Proc. 1st Int. Conf. Advancements of Medical Electronics ICAME 2015, Lecture Notes in Bioengineering, pages 49–57. Springer, Hamburg, Germany, 29-30 Jan 2015. https://doi.org/10.1007/978-81-322-2256-9_5. (Crossref)

J. Thiran and B. Macq. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on Biomedical Engineering, 43(10):1011–1020, 1996. https://doi.org/10.1109/10.536902. (Crossref)

Wikipedia contributors. Temperatura barwowa – Wikipedia, wolna encyklopedia, Nov 2019. https://pl.wikipedia.org/wiki/Temperatura_barwowa. [Accessed Oct 2020].

Wikipedia contributors. Color temperature – Wikipedia, The Free Encyclopedia, Dec 2020. https://en.wikipedia.org/wiki/Color_temperature. [Accessed Oct 2020].

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