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 Kovačević

doi:10.1117/12.893641

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 Kovačević

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Wavelets and Sparsity XIV
DOIs	https://doi.org/10.1117/12.893641
State	Published - 2011
Event	Wavelets and Sparsity XIV - San Diego, CA, United States Duration: Aug 21 2011 → Aug 24 2011

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8138
ISSN (Print)	0277-786X

Other

Other	Wavelets and Sparsity XIV
Country/Territory	United States
City	San Diego, CA
Period	8/21/11 → 8/24/11

Keywords

classification
local histogram
occlusion
segmentation
texture

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.893641

Cite this

Massar, M. L., Bhagavatula, R., Ozolek, J. A., Castro, C. A., Fickus, M., & Kovačević, J. (2011). A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images. In Wavelets and Sparsity XIV Article 81380U (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8138). https://doi.org/10.1117/12.893641

A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images. / Massar, Melody L.; Bhagavatula, Ramamurthy; Ozolek, John A. et al.
Wavelets and Sparsity XIV. 2011. 81380U (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8138).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Massar, ML, Bhagavatula, R, Ozolek, JA, Castro, CA, Fickus, M & Kovačević, J 2011, A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images. in Wavelets and Sparsity XIV., 81380U, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8138, Wavelets and Sparsity XIV, San Diego, CA, United States, 8/21/11. https://doi.org/10.1117/12.893641

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

Abstract

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Keywords

ASJC Scopus subject areas

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