Radial-basis-function based classification of mammographic microcalcifications using texture features

Atam P. Dhawan; Yateen Chitre; Christine Bonasso; Kevin Wheeler

Radial-basis-function based classification of mammographic microcalcifications using texture features

Atam P. Dhawan
, Yateen Chitre
, Christine Bonasso
, Kevin Wheeler

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

Abstract

Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30% to 50% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.

Original language	English (US)
Pages (from-to)	535-536
Number of pages	2
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	17
Issue number	1
State	Published - 1995
Externally published	Yes
Event	Proceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can Duration: Sep 20 1995 → Sep 23 1995

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Cite this

@article{09dc71045c1144eda89b45e109d2a521,

title = "Radial-basis-function based classification of mammographic microcalcifications using texture features",

abstract = "Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30\% to 50\% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.",

author = "Dhawan, \{Atam P.\} and Yateen Chitre and Christine Bonasso and Kevin Wheeler",

year = "1995",

language = "English (US)",

volume = "17",

pages = "535--536",

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

issn = "0589-1019",

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

number = "1",

note = "Proceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) ; Conference date: 20-09-1995 Through 23-09-1995",

}

Radial-basis-function based classification of mammographic microcalcifications using texture features. / Dhawan, Atam P.; Chitre, Yateen; Bonasso, Christine et al.
In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 17, No. 1, 1995, p. 535-536.

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

TY - JOUR

T1 - Radial-basis-function based classification of mammographic microcalcifications using texture features

AU - Dhawan, Atam P.

AU - Chitre, Yateen

AU - Bonasso, Christine

AU - Wheeler, Kevin

PY - 1995

Y1 - 1995

N2 - Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30% to 50% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.

AB - Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30% to 50% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.

UR - https://www.scopus.com/pages/publications/0029430297

UR - https://www.scopus.com/pages/publications/0029430297#tab=citedBy

M3 - Conference article

AN - SCOPUS:0029430297

SN - 0589-1019

VL - 17

SP - 535

EP - 536

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

IS - 1

T2 - Proceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2)

Y2 - 20 September 1995 through 23 September 1995

ER -

Radial-basis-function based classification of mammographic microcalcifications using texture features

Abstract

All Science Journal Classification (ASJC) codes

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