Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography

G. G. Lee, C. H. Chen

Research output: Contribution to conferencePaperpeer-review

5 Citations (Scopus)

Abstract

In this paper a novel multiresolution wavelet analysis (MWA) and non-stationary Gaussian Markov random field (GMRF) technique is introduced for the identification of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides a highly efficient technique for microcalcification detection. A Bayesian learning paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of larger lesions recorded on the mammograms. The effectiveness of the approach has been extensively tested with a number of mammographic images provided by a local hospital.

Original languageEnglish
Pages1737-1741
Number of pages5
Publication statusPublished - 1996
EventProceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3) - Anaheim, CA, USA
Duration: 1996 Nov 21996 Nov 9

Other

OtherProceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3)
CityAnaheim, CA, USA
Period96-11-0296-11-09

All Science Journal Classification (ASJC) codes

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

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