ELCVIA Electronic Letters on Computer Vision and Image Analysis (Aug 2009)

Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine

  • Leonardo de Oliveira Martins,
  • Geraldo Braz Junior,
  • Aristófanes Correa Silva,
  • Anselmo Cardoso de Paiva,
  • Marcelo Gattass

DOI
https://doi.org/10.5565/rev/elcvia.216
Journal volume & issue
Vol. 8, no. 2

Abstract

Read online

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aid radiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodology to detect masses from mammograms. The K-means clustering algorithm is used to split the mammograms in regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-mass region. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We use a set of textural and shape measures to detect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity, inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique. The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

Keywords