A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms

J. T. Neyhart; R. E. Eckert; R. Polikar; S. Mandayam; M. Tseng

A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms

J. T. Neyhart, R. E. Eckert, R. Polikar, S. Mandayam, M. Tseng

Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented - these have been independently validated by a radiologist.

Original language	English (US)
Pages (from-to)	995-996
Number of pages	2
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
State	Published - Dec 1 2002
Event	Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States Duration: Oct 23 2002 → Oct 26 2002

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Cite this

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title = "A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms",

abstract = "The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented - these have been independently validated by a radiologist.",

author = "Neyhart, {J. T.} and Eckert, {R. E.} and R. Polikar and S. Mandayam and M. Tseng",

year = "2002",

month = dec,

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language = "English (US)",

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pages = "995--996",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "1557-170X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) ; Conference date: 23-10-2002 Through 26-10-2002",

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TY - JOUR

T1 - A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms

AU - Neyhart, J. T.

AU - Eckert, R. E.

AU - Polikar, R.

AU - Mandayam, S.

AU - Tseng, M.

PY - 2002/12/1

Y1 - 2002/12/1

N2 - The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented - these have been independently validated by a radiologist.

AB - The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented - these have been independently validated by a radiologist.

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M3 - Conference article

AN - SCOPUS:0036906923

SN - 1557-170X

VL - 2

SP - 995

EP - 996

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS)

Y2 - 23 October 2002 through 26 October 2002

ER -

A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms

Abstract

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A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms

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