Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks

Stelios Halkiotis, Taxiarchis Botsis, Maria Rangoussi

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we propose a new algorithm for the detection of clustered microcalcifications using mathematical morphology and artificial neural networks. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a non-uniform background. Considering each mammogram as a topographic representation, each microcalcification appears as an elevation constituting a regional maximum. Morphological filters are applied, in order to remove: (a) noise and (b) regional maxima that do not correspond to calcifications. Each candidate object is marked as such, using a binary image. The original mammogram is used for the final feature extraction step. For the classification step we employ neural network classifiers. We review the performance of two multi-layer perceptrons (MLP) and two radial basis function neural networks (RBFNN) with different number of hidden nodes. The MLP with ten hidden nodes achieved the best classification score with a true positive detection rate of 94.7% and 0.27 false positives per image.

Original languageEnglish (US)
Pages (from-to)1559-1568
Number of pages10
JournalSignal Processing
Volume87
Issue number7
DOIs
StatePublished - Jul 2007
Externally publishedYes

Keywords

  • Dynamics
  • Mammography
  • Mathematical morphology
  • Microcalcifications
  • Multi-layer perceptron
  • Neural networks
  • Radial basis function networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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