Fully Automated Blind Color Deconvolution of Histopathological Images

  • Natalia Hidalgo-Gavira
  • Javier MateosEmail author
  • Miguel Vega
  • Rafael Molina
  • Aggelos K. Katsaggelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Most whole-slide histological images are stained with hematoxylin and eosin 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. Our model takes into account both spatial relations among image pixels and similarity to a given reference color-vector matrix. Using Variational Bayes inference, an efficient new blind color deconvolution method is proposed which provides a fully automated procedure to estimate all the unknowns in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms, using real images with known ground truth hematoxylin and eosin values, has been carried out demonstrating the superiority of the proposed approach.


Blind color deconvolution Histopathological images Bayesian modelling and inference Variational Bayes 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Natalia Hidalgo-Gavira
    • 1
  • Javier Mateos
    • 1
    Email author
  • Miguel Vega
    • 2
  • Rafael Molina
    • 1
  • Aggelos K. Katsaggelos
    • 3
  1. 1.Dpto. de Ciencias de la Computación e I. A.Universidad de GranadaGranadaSpain
  2. 2.Dpto. de Lenguajes y Sistemas InformáticosUniversidad de GranadaGranadaSpain
  3. 3.Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA

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