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Mixture Modeling for Digital Mammogram Display and Analysis

  • Stephen R. Aylward
  • Bradley M. Hemminger
  • Etta D. Pisano
Part of the Computational Imaging and Vision book series (CIVI, volume 13)

Abstract

We have devised a mammogram modeling system which greatly simplifies the development of, and can improve the accuracy and consistency of, computer-aided display and analysis algorithms for digital mammography. Our system segments the five major components of a mammogram: background, uncompressed-fat, fat, dense, and muscle. Differences in the amount and distribution of these components account for much of the variation between mammograms. Via segmentation, the corresponding variations are isolated; automated algorithms can consider the components independently or adapt their parameters based on component-specific statistics.

In this paper, we present our system and demonstrate its versatility. Our system is able to segment a wide variety of digital mammograms because of its combined use of geometric (i.e., gradient magnitude ridge traversal) and statistical (i.e., Gaussian mixture modeling) techniques. Using images from Fischer, General Electric, and Trex digital mammography units, we define and evaluate automated, component-based algorithms for (1) “general” intensity windowing, i.e., displaying a digital mammogram such that it resembles a screen-film mammogram for breast cancer screening; (2) component-specific intensity windowing for breast lesion characterization; and (3) breast density estimation for breast cancer risk assessment.

Keywords

Water Load Water Excretion Chronic Congestive Heart Failure Digital Mammogram Peritoneovenous Shunting 
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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References

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    Aylward S, Pizer S, Bullitt E, Eberly D (1996) Intensity Ridge and Widths for Tubular Object Segmentation and Description. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp 131–138.Google Scholar
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    Byrne C, Schairer C, Wolfe J, Parekh N, Salane M, Brinton L, Hoover R, Haile R (1995) Mammographic Features and Breast Cancer Risk: Effects with Time, Age, and Menopause Status. Journal of the National Cancer Institute 87(21), pp 1622–1629PubMedCrossRefGoogle Scholar
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    Karssemeijer N (1998) Automated Classification of Parenchymal Patterns in Mammograms. Phys. Med. Biol. 43, pp 365–378PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Stephen R. Aylward
    • 1
  • Bradley M. Hemminger
    • 1
  • Etta D. Pisano
    • 1
  1. 1.Department of Radiology Medical Image Display and Analysis GroupUniversity of North Carolina at Chapel HillUSA

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