A microcalcification detection using multi-layer support vector machine in Korean digital mammogram

Ju Won Kwon; Hokyoung Kang; Yong Man Ro; Sung Min Kim

doi:10.1007/978-3-540-36841-0_586

A microcalcification detection using multi-layer support vector machine in Korean digital mammogram

Ju Won Kwon
, Hokyoung Kang
, Yong Man Ro
, Sung Min Kim

Department of Biomedical Engineering

Korea Advanced Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A computer-aided diagnosis (CAD) system has been examined to reduce the effort of radiologist. In the mammogram, it is helpful to improve the diagnostic accuracy of malignancy microcalcifications in early stage of detecting breast cancer. In this paper, we propose a microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microcalcifications are malignant or benign tumors. The proposed microcalcification detection is divided into two steps, each of which uses a SVM classifier. First, potential ROIs (Region of interest) those are suspicious as malignant tumors are detected as a coarse detection level. And then, each ROI is classified whether it is malignant or not. The proposed algorithm is applied to the Korean digital mammogram. Experimental result showed that the proposed method would outperform conventional method using ANN (artificial neural networks).

Original language	English
Title of host publication	IFMBE Proceedings
Editors	Sun I. Kim, Tae Suk Suh
Publisher	Springer Verlag
Pages	2324-2327
Number of pages	4
Edition	1
ISBN (Print)	9783540368397
DOIs	https://doi.org/10.1007/978-3-540-36841-0_586
State	Published - 2007
Event	10th World Congress on Medical Physics and Biomedical Engineering, WC 2006 - Seoul, Korea, Republic of Duration: 27 Aug 2006 → 1 Sep 2006

Publication series

Name	IFMBE Proceedings
Number	1
Volume	14
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	10th World Congress on Medical Physics and Biomedical Engineering, WC 2006
Country/Territory	Korea, Republic of
City	Seoul
Period	27/08/06 → 1/09/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Mammography
Microcalcification
SVM

Access to Document

10.1007/978-3-540-36841-0_586

Cite this

@inproceedings{713eeea004a840ccaaa7ee8a8b6fcaff,

title = "A microcalcification detection using multi-layer support vector machine in Korean digital mammogram",

abstract = "A computer-aided diagnosis (CAD) system has been examined to reduce the effort of radiologist. In the mammogram, it is helpful to improve the diagnostic accuracy of malignancy microcalcifications in early stage of detecting breast cancer. In this paper, we propose a microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microcalcifications are malignant or benign tumors. The proposed microcalcification detection is divided into two steps, each of which uses a SVM classifier. First, potential ROIs (Region of interest) those are suspicious as malignant tumors are detected as a coarse detection level. And then, each ROI is classified whether it is malignant or not. The proposed algorithm is applied to the Korean digital mammogram. Experimental result showed that the proposed method would outperform conventional method using ANN (artificial neural networks).",

keywords = "Mammography, Microcalcification, SVM",

author = "Kwon, \{Ju Won\} and Hokyoung Kang and Ro, \{Yong Man\} and Kim, \{Sung Min\}",

note = "Publisher Copyright: {\textcopyright} International Federation for Medical and Biological Engineering 2007.; 10th World Congress on Medical Physics and Biomedical Engineering, WC 2006 ; Conference date: 27-08-2006 Through 01-09-2006",

year = "2007",

doi = "10.1007/978-3-540-36841-0\_586",

language = "English",

isbn = "9783540368397",

series = "IFMBE Proceedings",

publisher = "Springer Verlag",

number = "1",

pages = "2324--2327",

editor = "Kim, \{Sun I.\} and Suh, \{Tae Suk\}",

booktitle = "IFMBE Proceedings",

address = "Germany",

edition = "1",

}

Kwon, JW, Kang, H, Ro, YM & Kim, SM 2007, A microcalcification detection using multi-layer support vector machine in Korean digital mammogram. in SI Kim & TS Suh (eds), IFMBE Proceedings. 1 edn, IFMBE Proceedings, no. 1, vol. 14, Springer Verlag, pp. 2324-2327, 10th World Congress on Medical Physics and Biomedical Engineering, WC 2006, Seoul, Korea, Republic of, 27/08/06. https://doi.org/10.1007/978-3-540-36841-0_586

A microcalcification detection using multi-layer support vector machine in Korean digital mammogram. / Kwon, Ju Won; Kang, Hokyoung; Ro, Yong Man et al.
IFMBE Proceedings. ed. / Sun I. Kim; Tae Suk Suh. 1. ed. Springer Verlag, 2007. p. 2324-2327 (IFMBE Proceedings; Vol. 14, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A microcalcification detection using multi-layer support vector machine in Korean digital mammogram

AU - Kwon, Ju Won

AU - Kang, Hokyoung

AU - Ro, Yong Man

AU - Kim, Sung Min

PY - 2007

Y1 - 2007

N2 - A computer-aided diagnosis (CAD) system has been examined to reduce the effort of radiologist. In the mammogram, it is helpful to improve the diagnostic accuracy of malignancy microcalcifications in early stage of detecting breast cancer. In this paper, we propose a microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microcalcifications are malignant or benign tumors. The proposed microcalcification detection is divided into two steps, each of which uses a SVM classifier. First, potential ROIs (Region of interest) those are suspicious as malignant tumors are detected as a coarse detection level. And then, each ROI is classified whether it is malignant or not. The proposed algorithm is applied to the Korean digital mammogram. Experimental result showed that the proposed method would outperform conventional method using ANN (artificial neural networks).

AB - A computer-aided diagnosis (CAD) system has been examined to reduce the effort of radiologist. In the mammogram, it is helpful to improve the diagnostic accuracy of malignancy microcalcifications in early stage of detecting breast cancer. In this paper, we propose a microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microcalcifications are malignant or benign tumors. The proposed microcalcification detection is divided into two steps, each of which uses a SVM classifier. First, potential ROIs (Region of interest) those are suspicious as malignant tumors are detected as a coarse detection level. And then, each ROI is classified whether it is malignant or not. The proposed algorithm is applied to the Korean digital mammogram. Experimental result showed that the proposed method would outperform conventional method using ANN (artificial neural networks).

KW - Mammography

KW - Microcalcification

KW - SVM

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DO - 10.1007/978-3-540-36841-0_586

M3 - Conference contribution

AN - SCOPUS:84958268046

SN - 9783540368397

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BT - IFMBE Proceedings

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PB - Springer Verlag

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Y2 - 27 August 2006 through 1 September 2006

ER -

A microcalcification detection using multi-layer support vector machine in Korean digital mammogram

Abstract

Publication series

Conference

UN SDGs

Keywords

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