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Proceedings Paper

A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images
Author(s): Melody L. Massar; Ramamurthy Bhagavatula; John A. Ozolek; Carlos A. Castro; Matthew Fickus; Jelena Kovacevic
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Paper Abstract

We present the current state of our work on a mathematical framework for identification and delineation of histopathology images-local histograms and occlusion models. Local histograms are histograms computed over defined spatial neighborhoods whose purpose is to characterize an image locally. This unit of description is augmented by our occlusion models that describe a methodology for image formation. In the context of this image formation model, the power of local histograms with respect to appropriate families of images will be shown through various proved statements about expected performance. We conclude by presenting a preliminary study to demonstrate the power of the framework in the context of histopathology image classification tasks that, while differing greatly in application, both originate from what is considered an appropriate class of images for this framework.

Paper Details

Date Published: 27 September 2011
PDF: 7 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81380U (27 September 2011); doi: 10.1117/12.893641
Show Author Affiliations
Melody L. Massar, Air Force Institute of Technology (United States)
Ramamurthy Bhagavatula, Carnegie Mellon Univ. (United States)
John A. Ozolek, Children's Hospital of Pittsburgh (United States)
Carlos A. Castro, Univ. of Pittsburgh (United States)
Matthew Fickus, Air Force Institute of Technology (United States)
Jelena Kovacevic, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)

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