A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images

Melody L. Massar; Ramamurthy Bhagavatula; John A. Ozolek; Carlos A. Castro; Matthew Fickus; Jelena Kovacevic

doi:10.1117/12.893641

27 September 2011 A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images

Melody L. Massar, Ramamurthy Bhagavatula, John A. Ozolek, Carlos A. Castro, Matthew Fickus, Jelena Kovacevic

Author Affiliations +

Proceedings Volume 8138, Wavelets and Sparsity XIV; 81380U (2011) https://doi.org/10.1117/12.893641
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

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Melody L. Massar, Ramamurthy Bhagavatula, John A. Ozolek, Carlos A. Castro, Matthew Fickus, and Jelena Kovacevic "A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81380U (27 September 2011); https://doi.org/10.1117/12.893641

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