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

5 引文 (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 十二月 1
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

指紋

Mammography
Wavelet analysis
Edge detection
Screening

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

引用此文

Lee, G. G., & Chen, C. H. (1996). Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography. 1737-1741. 論文發表於 Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3), Anaheim, CA, USA, .
Lee, G. G. ; Chen, C. H. / Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography. 論文發表於 Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3), Anaheim, CA, USA, .5 p.
@conference{128a8796ceba442b98f3432739f7ad2f,
title = "Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography",
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.",
author = "Lee, {G. G.} and Chen, {C. H.}",
year = "1996",
month = "12",
day = "1",
language = "English",
pages = "1737--1741",
note = "Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3) ; Conference date: 02-11-1996 Through 09-11-1996",

}

Lee, GG & Chen, CH 1996, 'Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography', 論文發表於 Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3), Anaheim, CA, USA, 96-11-02 - 96-11-09 頁 1737-1741.

Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography. / Lee, G. G.; Chen, C. H.

1996. 1737-1741 論文發表於 Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3), Anaheim, CA, USA, .

研究成果: Paper

TY - CONF

T1 - Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography

AU - Lee, G. G.

AU - Chen, C. H.

PY - 1996/12/1

Y1 - 1996/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0030360714&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030360714&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0030360714

SP - 1737

EP - 1741

ER -

Lee GG, Chen CH. Multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography. 1996. 論文發表於 Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3), Anaheim, CA, USA, .