Variational Bayesian Blind Color Deconvolution of Histopathological Images

Natalia Hidalgo-Gavira, Javier Mateos*, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Article number8870230
Pages (from-to)2026-2036
Number of pages11
JournalIEEE Transactions on Image Processing
Volume29
Issue number1
DOIs
StatePublished - 2020

Keywords

  • Bayesian modeling and inference
  • Blind color deconvolution
  • histopathological images
  • variational Bayes

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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