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Classification of breast-tissue microarray spots using colour and local invariants

Classification of breast-tissue microarray spots using colour and local invariants

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Original languageEnglish
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
Place of PublicationNEW YORK
PublisherIEEE Computer Society
Pages999-1002
Number of pages4
ISBN (Print)978-1-4244-2002-5
DOIs
StatePublished - 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Paris, France

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Abbreviated titleISBI '08
CountryFrance
CityParis
Period14/05/0817/05/08
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Abstract

Breast tissue microarrays facilitate the survey of very large numbers of tumours but their scoring by pathologists is time consuming, typically highly quantised and not without error. Automated segmentation of cells and intra-cellular compartments in such data can be problematic for reasons that include cell overlapping, complex tissue structure, debris, and variable appearance. This paper proposes a computationally efficient approach that uses colour and differential invariants to assign class posterior probabilities to pixels and then performs probabilistic classification of TMA spots using features analogous to the Quickscore system currently used by pathologists. It does not rely on accurate segmentation of individual cells. Classification performance at both pixel and spot levels was assessed using 110 spots from the Adjuvant Breast Cancer (ABC) Chemotherapy Trial. The use of differential invariants in addition to colour yielded a small improvement in accuracy. Some reasons for classification results in disagreement with pathologist-provided labels are discussed and include noise in the class labels.

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