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15. Pectoral Muscle Removal Techniques from Digital Mammograms: A Survey

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Hela Boulehmi
Hela Mahersia
Kamel Hamrouni
Published in
Advances in Communication, Signal and Image Processing
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the critical task of pectoral muscle removal from digital mammograms, a key step in improving the accuracy of Computer-Aided Diagnosis (CAD) systems for early breast cancer detection. It reviews a range of image processing techniques developed since 2000, evaluated using mammograms from the MIAS database. The chapter highlights the challenges posed by the pectoral muscle's varying boundaries and densities, which can resemble breast lesions, leading to false positives. It explores methods such as the Hough transform, Gabor wavelets, and adaptive histogram equalization, among others, to effectively segment and remove the pectoral muscle. The chapter also presents illustrative examples of results obtained using these techniques and concludes with a comparative analysis, demonstrating that adaptive methods are more effective in achieving high detection rates. This comprehensive overview provides professionals with valuable insights into enhancing the accuracy and effectiveness of CAD systems in breast cancer diagnosis.
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previous chapter Generalized Gaussian Density: An Efficient Tool for Medical Images Texture Analysis
Title
Pectoral Muscle Removal Techniques from Digital Mammograms: A Survey
Authors
Hela Boulehmi
Hela Mahersia
Kamel Hamrouni
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Advances in Communication, Signal and Image Processing

Print ISBN: 978-981-9504-00-8

Electronic ISBN: 978-981-9504-01-5

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0401-5_15
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