A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens

Bonnie Hall, Wenjin Chen, Michael Reiss, David J. Foran

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

6 Scopus citations

Abstract

Motivated by the current limitations of automated quantitative image analysis in discriminating among intracellular immunohistochemical (IHC) staining patterns, this paper presents a two-fold approach for IHC characterization that utilizes both the protein stain information and the surrounding tissue architecture. Through the use of a color unmixing algorithm, stained tissue sections are automatically decomposed into the IHC stain, which visualizes the target protein, and the counterstain which provides an objective indication of the underlying histologic architecture. Feature measures are subsequently extracted from both staining planes. In order to characterize the IHC expression pattern, this approach exploits the use of a non-traditional feature based on textons. Novel biologically motivated filter banks are introduced in order to derive texture signatures for different IHC staining patterns. Systematic experiments using this approach were used to classify breast cancer tissue microarrays which had been previously prepared using immuno-targeted nuclear, cytoplasmic, and membrane stains.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings
PublisherSpringer Verlag
Pages287-294
Number of pages8
EditionPART 2
ISBN (Print)9783540757580
DOIs
StatePublished - 2007
Event10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia
Duration: Oct 29 2007Nov 2 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4792 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007
CountryAustralia
CityBrisbane
Period10/29/0711/2/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Automated classification
  • Breast cancer
  • Expression signatures
  • Quantitative IHC analysis
  • Texture descriptors

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