Robust contrast enhancement for microcalcification in mammography

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

doi:10.1007/978-3-540-24767-8_63

Robust contrast enhancement for microcalcification in mammography

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

Department of Biomedical Engineering

Korea Advanced Institute of Science and Technology

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

3 Scopus citations

Abstract

Microcalcification is important for early breast cancer detection. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. In this paper, we propose a robust contrast enhancement method for microcalcification. The proposed method is modified homomorphic filtering in wavelet domain based on background noise information. By using the proposed method, the mammogram contrast can be stretched adaptively thereby enhancing the contrast. Experimental results show that the proposed method improves the visibility of microcalcifications. The contrast improvement index (CII) is increased while noise standard deviation is decreased.

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	Antonio Lagan`a, Marina L. Gavrilova, Vipin Kumar, Youngsong Mun, C.J. Kenneth Tan, Osvaldo Gervasi
Publisher	Springer Verlag
Pages	602-610
Number of pages	9
ISBN (Print)	3540220577, 9783540220572
DOIs	https://doi.org/10.1007/978-3-540-24767-8_63
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3045
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

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10.1007/978-3-540-24767-8_63

Cite this

Kang, H. K., Thanh, N. N., Kim, S. M., & Ro, Y. M. (2004). Robust contrast enhancement for microcalcification in mammography. In A. Lagan`a, M. L. Gavrilova, V. Kumar, Y. Mun, C. J. Kenneth Tan, & O. Gervasi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 602-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3045). Springer Verlag. https://doi.org/10.1007/978-3-540-24767-8_63

Kang, Ho Kyung ; Thanh, Nguyen N. ; Kim, Sung Min et al. / Robust contrast enhancement for microcalcification in mammography. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Antonio Lagan`a ; Marina L. Gavrilova ; Vipin Kumar ; Youngsong Mun ; C.J. Kenneth Tan ; Osvaldo Gervasi. Springer Verlag, 2004. pp. 602-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Robust contrast enhancement for microcalcification in mammography",

abstract = "Microcalcification is important for early breast cancer detection. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. In this paper, we propose a robust contrast enhancement method for microcalcification. The proposed method is modified homomorphic filtering in wavelet domain based on background noise information. By using the proposed method, the mammogram contrast can be stretched adaptively thereby enhancing the contrast. Experimental results show that the proposed method improves the visibility of microcalcifications. The contrast improvement index (CII) is increased while noise standard deviation is decreased.",

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

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

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Kang, HK, Thanh, NN, Kim, SM & Ro, YM 2004, Robust contrast enhancement for microcalcification in mammography. in A Lagan`a, ML Gavrilova, V Kumar, Y Mun, CJ Kenneth Tan & O Gervasi (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. 3045, Springer Verlag, pp. 602-610. https://doi.org/10.1007/978-3-540-24767-8_63

Robust contrast enhancement for microcalcification in mammography. / Kang, Ho Kyung; Thanh, Nguyen N.; Kim, Sung Min et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Antonio Lagan`a; Marina L. Gavrilova; Vipin Kumar; Youngsong Mun; C.J. Kenneth Tan; Osvaldo Gervasi. Springer Verlag, 2004. p. 602-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3045).

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

TY - CHAP

T1 - Robust contrast enhancement for microcalcification in mammography

AU - Kang, Ho Kyung

AU - Thanh, Nguyen N.

AU - Kim, Sung Min

AU - Ro, Yong Man

PY - 2004

Y1 - 2004

N2 - Microcalcification is important for early breast cancer detection. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. In this paper, we propose a robust contrast enhancement method for microcalcification. The proposed method is modified homomorphic filtering in wavelet domain based on background noise information. By using the proposed method, the mammogram contrast can be stretched adaptively thereby enhancing the contrast. Experimental results show that the proposed method improves the visibility of microcalcifications. The contrast improvement index (CII) is increased while noise standard deviation is decreased.

AB - Microcalcification is important for early breast cancer detection. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. In this paper, we propose a robust contrast enhancement method for microcalcification. The proposed method is modified homomorphic filtering in wavelet domain based on background noise information. By using the proposed method, the mammogram contrast can be stretched adaptively thereby enhancing the contrast. Experimental results show that the proposed method improves the visibility of microcalcifications. The contrast improvement index (CII) is increased while noise standard deviation is decreased.

UR - https://www.scopus.com/pages/publications/35048849522

U2 - 10.1007/978-3-540-24767-8_63

DO - 10.1007/978-3-540-24767-8_63

M3 - Chapter

AN - SCOPUS:35048849522

SN - 3540220577

SN - 9783540220572

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

SP - 602

EP - 610

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

A2 - Lagan`a, Antonio

A2 - Gavrilova, Marina L.

A2 - Kumar, Vipin

A2 - Mun, Youngsong

A2 - Kenneth Tan, C.J.

A2 - Gervasi, Osvaldo

PB - Springer Verlag

ER -

Kang HK, Thanh NN, Kim SM, Ro YM. Robust contrast enhancement for microcalcification in mammography. In Lagan`a A, Gavrilova ML, Kumar V, Mun Y, Kenneth Tan CJ, Gervasi O, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 602-610. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24767-8_63

Robust contrast enhancement for microcalcification in mammography

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