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Proceedings Paper

Color normalization for robust evaluation of microscopy images
Author(s): Jan Švihlík; Jan Kybic; David Habart
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Paper 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.

Paper Details

Date Published: 22 September 2015
PDF: 6 pages
Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95992F (22 September 2015); doi: 10.1117/12.2188236
Show Author Affiliations
Jan Švihlík, Czech Technical Univ. in Prague (Czech Republic)
Univ. of Chemistry and Technology Prague (Czech Republic)
Jan Kybic, Czech Technical Univ. in Prague (Czech Republic)
David Habart, Institute for Clinical and Experimental Medicine (Czech Republic)


Published in SPIE Proceedings Vol. 9599:
Applications of Digital Image Processing XXXVIII
Andrew G. Tescher, Editor(s)

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