Digital mammography: Towards pectoral muscle removal via independent component analysis

N. Nicolaou, S. Petroudi, J. Georgiou, M. Polycarpou, M. Brady

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The extraction of features for automated assessment for breast cancer detection and diagnosis requires identification of the breast tissue. The pectoral muscle in medio-lateral oblique (MLO) mammogram images is one of the few landmarks in the breast. Yet, it can bias and affect the results of any mammogram processing method. To avoid such effects it is often necessary to automatically identify and segment the pectoral muscle prior to breast tissue image analysis. We propose the use of Independent Component Analysis (ICA) for identification and subsequent removal of the pectoral muscle. The identification is posed as classification of image subsections corresponding to pectoral muscle and breast tissue as represented by a set of ICA basis functions. Average classification rates 97.3% and 83.3% for pectoral muscle and breast tissue respectively have been obtained.

Original languageEnglish
Title of host publication4th IET International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2008
Edition540 CP
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event4th IET International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2008 - Santa Margherita Ligure, Italy
Duration: 14 Jul 200816 Jul 2008

Conference

Conference4th IET International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2008
Country/TerritoryItaly
CitySanta Margherita Ligure
Period14/07/0816/07/08

Keywords

  • Digital mammography
  • Independent component analysis
  • Pectoral muscle identification

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