Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

Scott Doyle, Shannon Agner, Anant Madabhushi, Michael Feldman, John Tomaszewski

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

174 Scopus citations

Abstract

In this paper we present a novel image analysis methodology for automatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, including textural and nuclear architecture based features, are extracted from a database of 48 breast biopsy tissue studies (30 cancerous and 18 benign images). Spectral clustering is used to reduce the dimensionality of the feature set. A support vector machine (SVM) classifier is used (1) to distinguish between cancerous and non-cancerous images, and (2) to distinguish between images containing low and high grades of cancer. Classification is repeated using different subsets of features to compare their performance. The system achieves a 95.8% accuracy in distinguishing cancer from non-cancer using texture-based characteristics (Gabor filter features), and 93.3% accuracy in distinguishing high from low grades of cancer using architectural features. In addition, we investigate the underlying manifold structure on which the different grades of breast cancer lie as revealed through spectral clustering. The manifold shows a smooth spatial transition from low to high grade breast cancer.

Original languageEnglish
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages496-499
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: May 14 2008May 17 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period05/14/0805/17/08

Keywords

  • Automated grading
  • Breast cancer
  • Histopathology
  • Image analysis

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    Doyle, S., Agner, S., Madabhushi, A., Feldman, M., & Tomaszewski, J. (2008). Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI (pp. 496-499). [4541041] (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). https://doi.org/10.1109/ISBI.2008.4541041