Structure-preserved color normalization for histological images

作者：Abhishek Vahadane, Tingying Peng, Shadi Albarqouni, Maximilian Baust, Katja Steiger, Anna Melissa Schlitter, Amit Sethi, Iréne Esposito, Nassir Navab · 年份：2015 · DOI：10.1109/isbi.2015.7164042 · 被引用次数：93 · 研究领域：AI in cancer detection、Cell Image Analysis Techniques、Image Processing Techniques and Applications

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.