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Using Wavelet-Based Features to Identify Masses in Dense Breast Parenchyma

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Digital Mammography (IWDM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4046))

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Abstract

Automated detection of masses on mammograms is challenged by the presence of dense breast parenchyma. The aim of this study is to investigate the feasibility of wavelet-based feature analysis in identifying spiculated and circumscribed masses in dense breast parenchyma. The method includes an edge detection step for breast border identification and employs Gaussian mixture modeling for dense parenchyma labeling. Subsequently, wavelet decomposition is performed and intensity as well as orientation features are extracted from approximation and detail subimages, respectively. Logistic regression analysis (LRA) is employed to differentiate spiculated and circumscribed masses from normal dense parenchyma. The proposed method is tested in 90 dense mammograms containing spiculated masses (30), circumscribed masses (30) and normal parenchyma (30). Free-response receiver operating characteristic (FROC) analysis is used to evaluate the performance of the method, achieving 83.3% sensitivity at 1.5 and 1.8 false positives per image for identifying spiculated and circumscribed masses, respectively.

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Authors and Affiliations

  1. Department of Medical Physics, School of Medicine, University of Patras, 265 00, Patras, Greece

    Filippos Sakellaropoulos, Spyros Skiadopoulos, Anna Karahaliou, Lena Costaridou & George Panayiotakis

Authors
  1. Filippos Sakellaropoulos
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  2. Spyros Skiadopoulos
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  3. Anna Karahaliou
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  4. Lena Costaridou
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  5. George Panayiotakis
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Editor information

Editors and Affiliations

  1. Division of Imaging Science and Biomedical Engineering, Stopford Building, University of Manchester, Oxford Road, M13 9PT, Manchester

    Susan M. Astley

  2. Wolfson Medical Vision Lab, University of Oxford, UK

    Michael Brady

  3. Imaging Science and Biomedical Engineeering, University of Manchester, Oxford Road, M13 9PT, Manchester, UK

    Chris Rose

  4. Department of Computer Science, University of Wales, Aberystwyth, UK

    Reyer Zwiggelaar

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© 2006 Springer-Verlag Berlin Heidelberg

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Sakellaropoulos, F., Skiadopoulos, S., Karahaliou, A., Costaridou, L., Panayiotakis, G. (2006). Using Wavelet-Based Features to Identify Masses in Dense Breast Parenchyma. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_75

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  • DOI: https://doi.org/10.1007/11783237_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35625-7

  • Online ISBN: 978-3-540-35627-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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