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Understanding Hessian-Based Density Scoring

  • Jakob Raundahl
  • Marco Loog
  • Mads Nielsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density.

No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. For serial, temporal analysis it is necessary to be able to detect more subtle changes.

In previous work, a method for measuring the effect of HRT w.r.t. changes in biological density in the breast is described. The method provides structural information orthogonal to intensity-based methods. Hessian-based features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined.

We have previously shown that this method can separate patients receiving HRT from patients receiving placebo. In this work, the focus is on deeper understanding of the methodology using tests on sets of artificial images of regular elongated structures.

Keywords

Breast Cancer Risk Hormone Replacement Therapy Mammographic Density Mammographic Breast Density Hormone Replacement Therapy Group 
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

  • Jakob Raundahl
    • 1
  • Marco Loog
    • 1
  • Mads Nielsen
    • 1
  1. 1.IT University of Copenhagen 

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