Capturing Microcalcification Patterns in Dense Parenchyma with Wavelet-Based Eigenimages

  • Nikolaos Arikidis
  • Spyros Skiadopoulos
  • Filippos Sakellaropoulos
  • George Panayiotakis
  • Lena Costaridou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)


A method is proposed based on the combination of wavelet analysis and principal component analysis (PCA). Microcalcification (MC) candidate regions are initially labeled using area and contrast criteria. Mallat’s redundant dyadic wavelet transform is used to analyze the frequency content of image patterns at horizontal and vertical directions. PCA is used to efficiently encode MC patterns in wavelet-decomposed images. Feature weights are computed from the projection of each candidate MC pattern at the wavelet-based principal components. To assess the effectiveness of the proposed method, the same analysis is carried out in original images. Candidate MC patterns are classified by means of Linear Discriminant Analysis (LDA). Free-response Receiver Operating Characteristic (FROC) curves are produced for identifying MC clusters. The highest performance is obtained when PCA is applied in wavelet decomposed images achieving 80% sensitivity at 0.5 false positives per image in a dataset with 50 subtle MC clusters in dense parenchyma.


Linear Discriminant Analysis Wavelet Coefficient Digital Mammogram Dense Parenchyma Computer Assist Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nikolaos Arikidis
    • 1
  • Spyros Skiadopoulos
    • 1
  • Filippos Sakellaropoulos
    • 1
  • George Panayiotakis
    • 1
  • Lena Costaridou
    • 1
  1. 1.Department of Medical Physics, School of MedicineUniversity of PatrasPatrasGreece

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