Adaptive microcalcification detection in computer aided diagnosis

Ho Kyung Kang; Sung Min Kim; Nguyen N. Thanh; Yong Man Ro; Won Ha Kim

doi:10.1007/978-3-540-25944-2_144

Adaptive microcalcification detection in computer aided diagnosis

Ho Kyung Kang
, Sung Min Kim
, Nguyen N. Thanh
, Yong Man Ro
, Won Ha Kim

Department of Biomedical Engineering

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

4 Scopus citations

Abstract

Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection method in mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection includes two parts. One is adaptive mammogram enhancement algorithm using homomorphic filtering in wavelet. The filter parameters are determined by background characteristics. The other is multi-stage microcalcification detection method. To verify our algorithm, we performed experiments and measured free-response operation characteristics (FROC) curve. The results show that the proposed microcalcification detection method is more robust for fluctuating noisy environments.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Marian Bubak, Geert Dick van Albada, Peter M. A. Sloot, Jack J. Dongarra
Publisher	Springer Verlag
Pages	1110-1117
Number of pages	8
ISBN (Print)	3540221298
DOIs	https://doi.org/10.1007/978-3-540-25944-2_144
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3039
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-25944-2_144

Cite this

Kang, H. K., Kim, S. M., Thanh, N. N., Ro, Y. M., & Kim, W. H. (2004). Adaptive microcalcification detection in computer aided diagnosis. In M. Bubak, G. D. van Albada, P. M. A. Sloot, & J. J. Dongarra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1110-1117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3039). Springer Verlag. https://doi.org/10.1007/978-3-540-25944-2_144

Kang, Ho Kyung ; Kim, Sung Min ; Thanh, Nguyen N. et al. / Adaptive microcalcification detection in computer aided diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Marian Bubak ; Geert Dick van Albada ; Peter M. A. Sloot ; Jack J. Dongarra. Springer Verlag, 2004. pp. 1110-1117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{2c79c755903b41e09fa64d4906da2cde,

title = "Adaptive microcalcification detection in computer aided diagnosis",

abstract = "Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection method in mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection includes two parts. One is adaptive mammogram enhancement algorithm using homomorphic filtering in wavelet. The filter parameters are determined by background characteristics. The other is multi-stage microcalcification detection method. To verify our algorithm, we performed experiments and measured free-response operation characteristics (FROC) curve. The results show that the proposed microcalcification detection method is more robust for fluctuating noisy environments.",

author = "Kang, \{Ho Kyung\} and Kim, \{Sung Min\} and Thanh, \{Nguyen N.\} and Ro, \{Yong Man\} and Kim, \{Won Ha\}",

year = "2004",

doi = "10.1007/978-3-540-25944-2\_144",

language = "English",

isbn = "3540221298",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1110--1117",

editor = "Marian Bubak and \{van Albada\}, \{Geert Dick\} and Sloot, \{Peter M. A.\} and Dongarra, \{Jack J.\}",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "Germany",

}

Kang, HK, Kim, SM, Thanh, NN, Ro, YM & Kim, WH 2004, Adaptive microcalcification detection in computer aided diagnosis. in M Bubak, GD van Albada, PMA Sloot & JJ Dongarra (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3039, Springer Verlag, pp. 1110-1117. https://doi.org/10.1007/978-3-540-25944-2_144

Adaptive microcalcification detection in computer aided diagnosis. / Kang, Ho Kyung; Kim, Sung Min; Thanh, Nguyen N. et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Marian Bubak; Geert Dick van Albada; Peter M. A. Sloot; Jack J. Dongarra. Springer Verlag, 2004. p. 1110-1117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3039).

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

TY - CHAP

T1 - Adaptive microcalcification detection in computer aided diagnosis

AU - Kang, Ho Kyung

AU - Kim, Sung Min

AU - Thanh, Nguyen N.

AU - Ro, Yong Man

AU - Kim, Won Ha

PY - 2004

Y1 - 2004

N2 - Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection method in mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection includes two parts. One is adaptive mammogram enhancement algorithm using homomorphic filtering in wavelet. The filter parameters are determined by background characteristics. The other is multi-stage microcalcification detection method. To verify our algorithm, we performed experiments and measured free-response operation characteristics (FROC) curve. The results show that the proposed microcalcification detection method is more robust for fluctuating noisy environments.

AB - Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection method in mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection includes two parts. One is adaptive mammogram enhancement algorithm using homomorphic filtering in wavelet. The filter parameters are determined by background characteristics. The other is multi-stage microcalcification detection method. To verify our algorithm, we performed experiments and measured free-response operation characteristics (FROC) curve. The results show that the proposed microcalcification detection method is more robust for fluctuating noisy environments.

UR - https://www.scopus.com/pages/publications/35048826720

U2 - 10.1007/978-3-540-25944-2_144

DO - 10.1007/978-3-540-25944-2_144

M3 - Chapter

AN - SCOPUS:35048826720

SN - 3540221298

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1110

EP - 1117

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Bubak, Marian

A2 - van Albada, Geert Dick

A2 - Sloot, Peter M. A.

A2 - Dongarra, Jack J.

PB - Springer Verlag

ER -

Kang HK, Kim SM, Thanh NN, Ro YM, Kim WH. Adaptive microcalcification detection in computer aided diagnosis. In Bubak M, van Albada GD, Sloot PMA, Dongarra JJ, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 1110-1117. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-25944-2_144

Adaptive microcalcification detection in computer aided diagnosis

Abstract

Publication series

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this