Statistical sequential analysis for detection of microcalcifications in digital mammograms

Jerzy Zielinski, Nidhal Bouaynaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We formulate the problem of microcalcification detection in digital mammograms as a statistical change detection problem in the local properties of the image. First, we represent mammograms by two-dimensional autoregressive moving-average (2D ARMA) fields; thus uniquely characterizing the images by their reduced dimensionality 2D ARMA feature vectors. Texture changes in mammograms are then modeled as an additive change in the mean parameter of the PDF associated with the 2D ARMA feature vector sequence that describes the image. A generalized likelihood ratio test is used to detect theses changes and estimate the exact time (or space) where they occur. Our simulation results on the Digital Database for Screening Mammography hosted by the University of South Florida show that the decision functions of cancerous images present high peaks at the microcalcification locations, whereas they exhibit a uniform behavior for healthy mammograms. The proposed algorithm achieves a sensitivity and specificity of 96.9% and 97.8%, respectively.

Original languageEnglish (US)
Title of host publication2010 International Conference on Signal Processing and Communications, SPCOM 2010
DOIs
StatePublished - Oct 29 2010
Externally publishedYes
Event2010 International Conference on Signal Processing and Communications, SPCOM 2010 - Bangalore, India
Duration: Jul 18 2010Jul 21 2010

Publication series

Name2010 International Conference on Signal Processing and Communications, SPCOM 2010

Other

Other2010 International Conference on Signal Processing and Communications, SPCOM 2010
Country/TerritoryIndia
CityBangalore
Period7/18/107/21/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing

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