Fully Automatic Blind Color Deconvolution of Histological Images Using Super Gaussians
Matlab code. See example.m for an use case over a single H&E image
F. Pérez-Bueno, M. Vega, V. Naranjo, R. Molina and A. K. Katsaggelos,
"Fully Automatic Blind Color Deconvolution of Histological Images Using Super Gaussians,"
2020 28th European Signal Processing Conference (EUSIPCO), 2021, pp. 1254-1258,
doi: https://doi.org/10.23919/Eusipco47968.2020.9287497
In digital pathology blind color deconvolution techniques separate multi-stained images into single stained bands. These band images are then used for image analysis and classification purposes. This paper proposes the use of Super Gaussian priors for each stain band together with the similarity to a given reference matrix for the color vectors. Variational inference and an evidence lower bound are then utilized to automatically estimate the latent variables and model parameters. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution. Its use as a preprocessing step in prostate cancer classification is also analysed.
@INPROCEEDINGS{9287497, author={Pérez-Bueno, Fernando and Vega, Miguel and Naranjo, Valery and Molina, Rafael and Katsaggelos, Aggelos K.}, booktitle={2020 28th European Signal Processing Conference (EUSIPCO)}, title={Fully Automatic Blind Color Deconvolution of Histological Images Using Super Gaussians}, year={2021}, volume={}, number={}, pages={1254-1258}, doi={10.23919/Eusipco47968.2020.9287497}}
If you are interested in blind color deconvolution you might be interested in our works:
- A TV-based Image Processing Framework for Blind Color Deconvolution and Classification of Histological Images. Digital Signal Processing, 2020\
- "Variational Bayesian Blind Color Deconvolution of Histopathological Images," in IEEE Transactions on Image Processing, vol. 29, pp. 2026-2036, 2020