Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach. Hidalgo-Gavira, N., Mateos, J., Vega, M., Molina, R., & Katsaggelos, A. K. In 2018 25th IEEE International Conference on Image Processing (ICIP), volume 29, pages 983–987, oct, 2018. IEEE.
Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach [link]Paper  doi  abstract   bibtex   
Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach.
@inproceedings{Natalia2018,
abstract = {Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach.},
author = {Hidalgo-Gavira, Natalia and Mateos, Javier and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2018.8451314},
isbn = {978-1-4799-7061-2},
issn = {1057-7149},
keywords = {Bayesian modeling and inference,Blind color deconvolution,histopathological images,variational Bayes},
month = {oct},
number = {1},
pages = {983--987},
pmid = {31634128},
publisher = {IEEE},
title = {{Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach}},
url = {https://ieeexplore.ieee.org/document/8870230/ https://ieeexplore.ieee.org/document/8451314/},
volume = {29},
year = {2018}
}

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