Color normalization for robust evaluation of microscopy images

Jan Švihlík; Jan Kybic; David Habart

doi:10.1117/12.2188236

22 September 2015 Color normalization for robust evaluation of microscopy images

Jan Švihlík, Jan Kybic, David Habart

Author Affiliations +

Proceedings Volume 9599, Applications of Digital Image Processing XXXVIII; 95992F (2015) https://doi.org/10.1117/12.2188236
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

This paper deals with color normalization of microscopy images of Langerhans islets in order to increase robustness of the islet segmentation to illumination changes. The main application is automatic quantitative evaluation of the islet parameters, useful for determining the feasibility of islet transplantation in diabetes. First, background illumination inhomogeneity is compensated and a preliminary foreground/background segmentation is performed. The color normalization itself is done in either lαβ or logarithmic RGB color spaces, by comparison with a reference image. The color-normalized images are segmented using color-based features and pixel-wise logistic regression, trained on manually labeled images. Finally, relevant statistics such as the total islet area are evaluated in order to determine the success likelihood of the transplantation.

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Jan Švihlík, Jan Kybic, and David Habart "Color normalization for robust evaluation of microscopy images", Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95992F (22 September 2015); https://doi.org/10.1117/12.2188236

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