Structure-preserved color normalization for histological images

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.

[1]  Joakim Lindblad,et al.  Blind Color Decomposition of Histological Images , 2013, IEEE Transactions on Medical Imaging.

[2]  Derek R. Magee,et al.  Colour Normalisation in Digital Histopathology Images , 2009 .

[3]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[4]  J. S. Marron,et al.  A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[6]  Serge J. Belongie,et al.  Unsupervised Color Decomposition Of Histologically Stained Tissue Samples , 2003, NIPS.

[7]  Guillermo Sapiro,et al.  Online dictionary learning for sparse coding , 2009, ICML '09.

[8]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[9]  Nassir Navab,et al.  Leveraging Random Forests for Interactive Exploration of Large Histological Images , 2014, MICCAI.

[10]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[11]  Nasir M. Rajpoot,et al.  A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.

[12]  Ting Chen,et al.  Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images , 2014, MLMI.