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

G. G. Lee, C. H. Chen

研究成果: Paper同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁面1737-1741
頁數5
出版狀態Published - 1996
事件Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3) - Anaheim, CA, USA
持續時間: 1996 十一月 21996 十一月 9

Other

OtherProceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3)
城市Anaheim, CA, USA
期間96-11-0296-11-09

All Science Journal Classification (ASJC) codes

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

指紋 深入研究「Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography」主題。共同形成了獨特的指紋。

引用此