Structure-preserved color normalization for histological images

Abhishek Vahadane(Technical University of Munich), Tingying Peng(Technical University of Munich), Shadi Albarqouni(Technical University of Munich), Maximilian Baust(Technical University of Munich), Katja Steiger(Technical University of Munich), Anna Melissa Schlitter(Technical University of Munich), Amit Sethi(Indian Institute of Technology Guwahati), Iréne Esposito(Universität Innsbruck), Nassir Navab(Johns Hopkins University)
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April 1, 2015
Cited by 93

Abstract

Automated image processing and quantification are increasingly gaining attention in the field of digital pathology. However, a common problem that encumbers computerized analysis is the color variation in histology, due to the use of different microscopes/scanners, or inconsistencies in tissue preparation. In this paper, we present a novel color normalization technique to bring a histological image (source image) into a different color appearance of a second image (target image), which therefore standardizes the color representation of both images. In particular, by incorporating biological stain-sparse regularized stain separation, our color normalization technique preserves the structural information of the source image after color normalization, which is very important for subsequent image analysis. Both qualitative and quantitative validation demonstrates the superior performance of our stain separation and color normalization techniques.


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