A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation
This paper presents a method for breast cancer diagnosis in digital mammogram images. The wavelet is used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet coefficients of each image in row vector, where the number of row is the num...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/2171/ http://scholars.utp.edu.my/id/eprint/2171/1/Paper_USA.pdf |
| _version_ | 1848659209891086336 |
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| author | Brahim Belhaouari, samir Ibrahima, faye Mohamed, meselhy |
| author_facet | Brahim Belhaouari, samir Ibrahima, faye Mohamed, meselhy |
| author_sort | Brahim Belhaouari, samir |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | This paper presents a method for breast cancer
diagnosis in digital mammogram images. The wavelet is
used to transform the mammogram images into a long
vector of coefficients. A matrix is constructed by putting
wavelet coefficients of each image in row vector, where
the number of row is the number of images, and the
number of columns is the number of coefficients. A feature
extraction method is developed based on the statistical ttest
method. The method is ranking the columns
(features) according to its capability to distinguish
between the different classes. The method depends on
extracting the features that can maximize the ability to
discriminate between different classes. Thus, the
dimensionality of data features is reduced and the
classification accuracy rate is improved. Then a dynamic
threshold is applied to optimize the number of feature
which can achieve the maximum classification accuracy
rate. Support vector machine (SVM) is used to classify
between the normal and abnormal and to distinguish
between benign and malignant. The obtained
classification accuracy rates reach to 100%. |
| first_indexed | 2025-11-13T07:26:48Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:2171 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:26:48Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:21712017-01-19T08:23:52Z http://scholars.utp.edu.my/id/eprint/2171/ A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation Brahim Belhaouari, samir Ibrahima, faye Mohamed, meselhy TK Electrical engineering. Electronics Nuclear engineering This paper presents a method for breast cancer diagnosis in digital mammogram images. The wavelet is used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet coefficients of each image in row vector, where the number of row is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical ttest method. The method is ranking the columns (features) according to its capability to distinguish between the different classes. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Then a dynamic threshold is applied to optimize the number of feature which can achieve the maximum classification accuracy rate. Support vector machine (SVM) is used to classify between the normal and abnormal and to distinguish between benign and malignant. The obtained classification accuracy rates reach to 100%. 2010-08-01 Conference or Workshop Item PeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/2171/1/Paper_USA.pdf Brahim Belhaouari, samir and Ibrahima, faye and Mohamed, meselhy (2010) A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation. In: INTERNATIONAL CONFERENCE ON IMAGE AND VIDEO PROCESSING AND COMPUTER VISION. |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Brahim Belhaouari, samir Ibrahima, faye Mohamed, meselhy A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation |
| title | A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in
Digital Mammogram Using Multiresolution Representation |
| title_full | A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in
Digital Mammogram Using Multiresolution Representation |
| title_fullStr | A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in
Digital Mammogram Using Multiresolution Representation |
| title_full_unstemmed | A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in
Digital Mammogram Using Multiresolution Representation |
| title_short | A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in
Digital Mammogram Using Multiresolution Representation |
| title_sort | statistical based feature extraction method for breast cancer diagnosis in
digital mammogram using multiresolution representation |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://scholars.utp.edu.my/id/eprint/2171/ http://scholars.utp.edu.my/id/eprint/2171/1/Paper_USA.pdf |