Super Gaussian Priors for Blind Color Deconvolution of Histological Images. Perez-Bueno, F., Vega, M., Naranjo, V., Molina, R., & Katsaggelos, A. K. In 2020 IEEE International Conference on Image Processing (ICIP), volume 2020-Octob, pages 3010–3014, oct, 2020. IEEE. ![Super Gaussian Priors for Blind Color Deconvolution of Histological Images [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Color deconvolution aims at separating multi-stained images into single stained ones. In digital histopathological images, true stain color vectors vary between images and need to be estimated to obtain stain concentrations and separate stain bands. These band images can be used for image analysis purposes and, once normalized, utilized with other multi-stained images (from different laboratories and obtained using different scanners) for classification purposes. In this paper we propose the use of Super Gaussian (SG) priors for each stain concentration together with the similarity to a given reference matrix for the color vectors. Variational inference and an evidence lower bound are utilized to automatically estimate all the latent variables. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution.
@inproceedings{Fernando2020a,
abstract = {Color deconvolution aims at separating multi-stained images into single stained ones. In digital histopathological images, true stain color vectors vary between images and need to be estimated to obtain stain concentrations and separate stain bands. These band images can be used for image analysis purposes and, once normalized, utilized with other multi-stained images (from different laboratories and obtained using different scanners) for classification purposes. In this paper we propose the use of Super Gaussian (SG) priors for each stain concentration together with the similarity to a given reference matrix for the color vectors. Variational inference and an evidence lower bound are utilized to automatically estimate all the latent variables. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution.},
author = {Perez-Bueno, Fernando and Vega, Miguel and Naranjo, Valery and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP40778.2020.9191200},
isbn = {978-1-7281-6395-6},
issn = {15224880},
keywords = {Blind Color Deconvolution,Histopathological Images,Super Gaussian,Variational Bayes},
month = {oct},
pages = {3010--3014},
publisher = {IEEE},
title = {{Super Gaussian Priors for Blind Color Deconvolution of Histological Images}},
url = {https://ieeexplore.ieee.org/document/9191200/},
volume = {2020-Octob},
year = {2020}
}
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