Local histograms for classifying H&E stained tissues

M. L. Massar, R. Bhagavatula, M. Fickus, J. Kovačević

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


We introduce a rigorous mathematical theory for the analysis of local histograms, and consider the appropriateness of their use in the automated classification of textures commonly encountered in images of H&E stained tissues. We first discuss some of the many image features that pathologists indicate they use when classifying tissues, focusing on simple, locally-defined features that essentially involve pixel counting: the number of cells in a region of given size, the size of the nuclei within these cells, and the distribution of color within both. We then introduce a probabilistic, occlusion-based model for textures that exhibit these features, in particular demonstrating how certain tissue-similar textures can be built up from simpler ones. After considering the basic notions and properties of local histogram transforms, we then formally demonstrate that such transforms are natural tools for analyzing the textures produced by our model. In particular, we discuss how local histogram transforms can be used to produce numerical features that, when fed into mainstream classification schemes, mimic the baser aspects of a pathologist's thought process.

Original languageEnglish (US)
Title of host publication26th Southern Biomedical Engineering Conference SBEC 2010
Number of pages5
StatePublished - 2010
Event26th Southern Biomedical Engineering Conference, SBEC 2010 - College Park, MD, United States
Duration: Apr 30 2010May 2 2010

Publication series

NameIFMBE Proceedings
Volume32 IFMBE
ISSN (Print)1680-0737


Other26th Southern Biomedical Engineering Conference, SBEC 2010
Country/TerritoryUnited States
CityCollege Park, MD


  • histology
  • local histogram
  • occlusion

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering


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