Mammographic Ellispe Modelling for Risk Estimation

Minu George, Erika Denton, Reyer Zwiggelaar

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Abstract

It has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk
Original languageEnglish
Pages (from-to)163-168
Number of pages6
JournalProcedia Computer Science
Volume90
DOIs
Publication statusPublished - 06 Jul 2016
EventMedical Imaging Understanding and Analysis - Loughborough University, Loughborough, United Kingdom of Great Britain and Northern Ireland
Duration: 06 Jul 201608 Jul 2016
Conference number: 20

Keywords

  • breast density modelling
  • blob and ellipse detection
  • breast density classification
  • mammographic risk estimation

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