Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method
This paper introduces a new method of feature extraction from Wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting Wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which...
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my.utp.eprints.9372017-01-19T08:27:45Z Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method Brahim Belhaouari, samir Ibrahima, Faye TK Electrical engineering. Electronics Nuclear engineering This paper introduces a new method of feature extraction from Wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting Wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns are then used as features for classification. The method is tested using a set of images provided by the Mammographic Image Analysis Society (MIAS) to classify between normal and abnormal and then between benign and malignant tissues. For both classifications, a high accuracy rate (98%) is achieved. Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/937/1/New_Feature_Extaction_Method.pdf Brahim Belhaouari, samir and Ibrahima, Faye Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method. In: the International Conference on Intelligent & Advanced Systems 2010. http://eprints.utp.edu.my/937/ |
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TK Electrical engineering. Electronics Nuclear engineering Brahim Belhaouari, samir Ibrahima, Faye Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method |
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This paper introduces a new method of feature extraction from Wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting Wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns are then used as features for classification. The method is tested using a set of images provided by the Mammographic Image Analysis Society (MIAS) to classify between normal and abnormal and then between benign and malignant tissues. For both classifications, a high accuracy rate (98%) is achieved. |
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Conference or Workshop Item |
author |
Brahim Belhaouari, samir Ibrahima, Faye |
author_facet |
Brahim Belhaouari, samir Ibrahima, Faye |
author_sort |
Brahim Belhaouari, samir |
title |
Digital Mammograms Classification Using a Wavelet Based
Feature Extraction Method |
title_short |
Digital Mammograms Classification Using a Wavelet Based
Feature Extraction Method |
title_full |
Digital Mammograms Classification Using a Wavelet Based
Feature Extraction Method |
title_fullStr |
Digital Mammograms Classification Using a Wavelet Based
Feature Extraction Method |
title_full_unstemmed |
Digital Mammograms Classification Using a Wavelet Based
Feature Extraction Method |
title_sort |
digital mammograms classification using a wavelet based
feature extraction method |
url |
http://eprints.utp.edu.my/937/1/New_Feature_Extaction_Method.pdf http://eprints.utp.edu.my/937/ |
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1738655102820941824 |
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13.18916 |