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Detection of Breast Cancer Based on Texture Analysis from Digital Mammograms

  • Eun-Byeol Jo
  • Ju-Hwan Lee
  • Jun-Young Park
  • Sung-Min Kim
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)

Abstract

In this study, we propose a novel breast cancer detection algorithm based on texture properties of mass area. Proposed method extracts the midpoint of mass area by using AHE (Adaptive Histogram Equalization), and selects the ROI (Region of Interest) in the original image. L1-norm based smoothing filter is then employed to stabilize the mass area, and the form of the mass is determined. Additionally, we measured homogeneity and Ranklet using SVM (Support Vector Machine) to analyze texture properties of the selected mass area. As a result, we observed that the proposed method shows the more stable and outstanding performance for Korean women compared with the existing methods.

Keywords

mammogram breast cancer SVM (Support Vector Machine) Ranklet homogeneity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eun-Byeol Jo
    • 1
  • Ju-Hwan Lee
    • 1
  • Jun-Young Park
    • 1
  • Sung-Min Kim
    • 1
  1. 1.Dept. of Medical Bio TechnologyDongguk University-SeoulSeoulKorea

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