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WAVELET METHODS IN IMPROVING THE DETECTION OF LESIONS IN MAMMOGRAMS

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Computer Vision and Graphics

Part of the book series: Computational Imaging and Vision ((CIVI,volume 32))

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Abstract

This paper presents a method for wavelet processing of mammogram images in order to highlight pathological changes which are important for diagnosing breast cancer. The method was used at the image pre-processing stage in a CAD (Computer Aided Detection) system which is under development. The paper presents the preliminary detection results for pathological areas with and without the method under discussion.

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REFERENCES

  1. Betal D., Roberts N., Whitehouse G.H., (1997), “Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology”, British J. of Radiology 70, 903–917.

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  2. Heinlein P., Drexl J., Schneider W., (2003), “Integrated Wavelets for Enhancement of Microcalcifications in Digital Mammography”, IEEE Trans. Medical Imag. 22(3), 402–413.

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  3. http://marathon.csee.usf.edu/Mammography/Database.html.

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  4. Przelaskowski A. (2002), “Falkowe metody kompresji danych obrazowych” Oficyna Wydawnicza Politechniki Warszawskiej.

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  5. Periaswamy S., (1996), “Detection of microcalcification in mammograms using hexagonal wavelets”, University of South Carolina.

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  6. A. Laine, S. Shuler (2003) “Hexagonal wavelet processing of digital mammography”, Gainesville, Florida 32611–2024.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Pawel Bargiel, Artur Przelaskowski & Anna Wroblewska

Authors
  1. Pawel Bargiel
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  2. Artur Przelaskowski
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  3. Anna Wroblewska
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Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    K. Wojciechowski

  2. Silesian University of Technology, Gliwice, Poland

    B. Smolka

  3. Silesian University of Technology, Gliwice, Poland

    H. Palus

  4. The University of Western Australia, Crawley, Australia

    R.S. Kozera

  5. Warsaw University of Technology, Warsaw, Poland

    W. Skarbek

  6. The University of Western Australia, Crawley, Australia

    L. Noakes

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© 2006 Springer

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Bargiel, P., Przelaskowski, A., Wroblewska, A. (2006). WAVELET METHODS IN IMPROVING THE DETECTION OF LESIONS IN MAMMOGRAMS. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_127

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  • DOI: https://doi.org/10.1007/1-4020-4179-9_127

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-4178-5

  • Online ISBN: 978-1-4020-4179-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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