A Contour Based Approach for Bilateral Mammogram Registration Using Discrete Wavelet Analysis

  • Ramon Reig-Bolaño
  • Vicenç Parisi Baradad
  • Pere Marti-Puig
Part of the Advances in Soft Computing book series (AINSC, volume 50)

Abstract

Registration of mammograms is an essential step to increase the effectiveness of all screening programs planned to early detect breast cancer in asymptomatic women. New techniques based on data fusion of different spatial views, temporal analysis or sequences follow up, and multimodal data analysis, require accurate data registration: forcing spatial alignment of images taken from different views, at different times or from different natures. In this work we will focus on a new technique for automatic bilateral mammograms registration. However the same registration technique is also useful for temporal analysis of the same patient. Bilateral mammogram registration is a challenging task; the mammographic appearance of breast tissue may vary considerably, because of differences in breast compression and positioning, differences in imaging techniques, and changes in the breast itself; moreover, there are no clear landmarks in a mammogram, except for the nipple when it is visible. In our approach we detect the skin-line contour as a first step, we describe the contour as a chain-code, and we make a wavelet based correlation analysis, finally we get the matching of the contours. At the end we apply a global affine transformation.

Keywords

medical imaging image registration image alignment wavelets transforms multiresolution analysis signal processing image processing 

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References

  1. 1.
    van Engeland, S., Snoeren, P., Hendriks, J., Karssemeijer, N.A.: Comparison of Methods for Mammogram Registration. IEEE Trans. on medical imaging 22, 11 (2003)CrossRefGoogle Scholar
  2. 2.
    Wirth, M.A., Narhan, J., Gray, D.: Nonrigid mammogram registration using mutual information. In: SPIE Medical Imaging: Image Processing, San Diego, USA, vol. 4684, pp. 562–573 (2002)Google Scholar
  3. 3.
    Guo, Y., Suri, J., Sivaramakrishoa, R.: Image Registration for Breast Imaging: A Review. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society IEEE-EMBS, vol. 3379, 3382 (2005)Google Scholar
  4. 4.
    Suckling, J., et al.: The Mammographic Image Analysis Society Digital Mammogram Database. Exerpta Medica. International Congress Series 1069, 375–378 (1995)Google Scholar
  5. 5.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, New York (1982)MATHGoogle Scholar
  6. 6.
    Li, H., Manjunath, B.S., Mitra, S.K.: A contour-based approach to multisensor image registration. IEEE Transactions on image Processing 4(3), 320–334 (1995)CrossRefGoogle Scholar
  7. 7.
    Dijamdji, J.P., Bijaoui, A.: Disparity Analysis: A Wavelet Transform Approach. IEEE Transactions on Geoscience and Remote Sensing 33(1) (1995)Google Scholar
  8. 8.
    PEIPA, the Pilot European Image Processing Archive, http://peipa.essex.ac.uk/

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ramon Reig-Bolaño
    • 1
  • Vicenç Parisi Baradad
    • 2
  • Pere Marti-Puig
    • 1
  1. 1.Department of Digital Information and TechnologiesUniversity of Vic (UVIC)Vic, BarcelonaSpain
  2. 2.Department of Electronic EngineeringPolitechnical Univerity of Catalonia (UPC)Vilanova i la Geltrú, BarcelonaSpain

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