2015 · 93 citations · 11 references
MedicinePathologyBiomedical ImagingHistologyHistopathologyColor ImagingHistological StainingImage QuantificationMedical Image AnalysisSpecimen PreparationColor NormalizationDigital Pathology AnalysisHistopathological Image AnalysisHistopathology Image AnalysisPathology Image AnalysisStain Standardization
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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Learning the parts of objects by non-negative matrix factorization
Daniel D. Lee, H. Sebastian Seung · Nature · 1999 · 14.1K citations
Fundamentals of digital image processing
Computer Vision Graphics and Image Processing · 1989 · 4.7K citations
Erik Reinhard, M. Adhikhmin, Bryan N S Gooch et al. · IEEE Computer Graphics and Applications · 2001 · 3K citations
Arts, Vision Science, Color Theory +10
Online dictionary learning for sparse coding
Julien Mairal, Francis Bach, Jean Ponce et al. · 2009 · 2.1K citations · Full text
Quantification of histochemical staining by color deconvolution.
Arnout Ruifrok, D. A. Johnston · PubMed · 2001 · 2.1K citations
Medicine, Cell Biology, Pathology +17