Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

Mohamed Meselhy Eltoukhy; Ibrahima Faye; Brahim Belhaouari Samir

doi:10.1016/j.compmedimag.2009.11.002

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

Mohamed Meselhy Eltoukhy^*, Ibrahima Faye, Brahim Belhaouari Samir

^*Corresponding author for this work

Universiti Teknologi Petronas

Research output: Contribution to journal › Article › peer-review

112 Citations (Scopus)

Abstract

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.

Original language	English
Pages (from-to)	269-276
Number of pages	8
Journal	Computerized Medical Imaging and Graphics
Volume	34
Issue number	4
DOIs	https://doi.org/10.1016/j.compmedimag.2009.11.002
Publication status	Published - Jun 2010
Externally published	Yes

Keywords

Breast cancer diagnosis
Curvelet transform
Digital mammogram
Feature extraction
Multiresolution

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10.1016/j.compmedimag.2009.11.002

Cite this

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title = "Breast cancer diagnosis in digital mammogram using multiscale curvelet transform",

abstract = "This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59\% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.",

keywords = "Breast cancer diagnosis, Curvelet transform, Digital mammogram, Feature extraction, Multiresolution",

author = "Eltoukhy, \{Mohamed Meselhy\} and Ibrahima Faye and Samir, \{Brahim Belhaouari\}",

year = "2010",

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journal = "Computerized Medical Imaging and Graphics",

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AU - Eltoukhy, Mohamed Meselhy

AU - Faye, Ibrahima

AU - Samir, Brahim Belhaouari

PY - 2010/6

Y1 - 2010/6

N2 - This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.

AB - This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.

KW - Breast cancer diagnosis

KW - Curvelet transform

KW - Digital mammogram

KW - Feature extraction

KW - Multiresolution

UR - https://www.scopus.com/pages/publications/77952237386

U2 - 10.1016/j.compmedimag.2009.11.002

DO - 10.1016/j.compmedimag.2009.11.002

M3 - Article

C2 - 20004076

AN - SCOPUS:77952237386

SN - 0895-6111

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SP - 269

EP - 276

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

IS - 4

ER -

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

Abstract

Keywords

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