Fully automatic blind color deconvolution of histological images using super gaussians

Fernando Pérez-Bueno, Miguel Vega, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1254-1258
Number of pages5
ISBN (Electronic)9789082797053
DOIs
StatePublished - Jan 24 2021
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: Aug 24 2020Aug 28 2020

Publication series

NameEuropean Signal Processing Conference
Volume2021-January
ISSN (Print)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period8/24/208/28/20

Keywords

  • Blind color deconvolution
  • Histopathological images
  • Super Gaussian
  • Variational Bayes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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