Super Gaussian Priors for Blind Color Deconvolution of Histological Images

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

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

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages3010-3014
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: Sep 25 2020Sep 28 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
CountryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period9/25/209/28/20

Keywords

  • Blind Color Deconvolution
  • Histopathological Images
  • Super Gaussian
  • Variational Bayes

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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