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

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available