Mammogram classification using curvelet GLCM texture features and GIST features
This paper presents a feature fusion technique that can be used for classification of ROIs in breast cancer into normal and abnormal classes. The texture features are extracted using geometric invariant shift transform and statistical features from the curvelet grey level co-occurrence matrices. Fir...
| Main Authors: | Gardezi, S.J.S., Faye, I., Adjed, F., Kamel, N., Eltoukhy, M.M. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.20333 / |
| Published: |
Springer Verlag
2017
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994508050&doi=10.1007%2f978-3-319-48308-5_67&partnerID=40&md5=be837cf29475b0685f78e75292854546 http://eprints.utp.edu.my/20333/ |
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utp-eprints.203332018-04-23T01:04:49Z Mammogram classification using curvelet GLCM texture features and GIST features Gardezi, S.J.S. Faye, I. Adjed, F. Kamel, N. Eltoukhy, M.M. This paper presents a feature fusion technique that can be used for classification of ROIs in breast cancer into normal and abnormal classes. The texture features are extracted using geometric invariant shift transform and statistical features from the curvelet grey level co-occurrence matrices. First classification accuracy of both methods were evaluated independently. Later, feature fusion is done to improve the classification performance. Support vector machine classifier with polynomial kernel was implemented using 2 × 5 folds cross validation. Fusion of features produces better results with accuracy of 92.39 as compared to 77.97 and 91 for GIST and CGLCM respectively. © Springer International Publishing AG 2017. Springer Verlag 2017 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994508050&doi=10.1007%2f978-3-319-48308-5_67&partnerID=40&md5=be837cf29475b0685f78e75292854546 Gardezi, S.J.S. and Faye, I. and Adjed, F. and Kamel, N. and Eltoukhy, M.M. (2017) Mammogram classification using curvelet GLCM texture features and GIST features. Advances in Intelligent Systems and Computing, 533 . pp. 705-713. http://eprints.utp.edu.my/20333/ |
| institution |
Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
This paper presents a feature fusion technique that can be used for classification of ROIs in breast cancer into normal and abnormal classes. The texture features are extracted using geometric invariant shift transform and statistical features from the curvelet grey level co-occurrence matrices. First classification accuracy of both methods were evaluated independently. Later, feature fusion is done to improve the classification performance. Support vector machine classifier with polynomial kernel was implemented using 2 × 5 folds cross validation. Fusion of features produces better results with accuracy of 92.39 as compared to 77.97 and 91 for GIST and CGLCM respectively. © Springer International Publishing AG 2017. |
| format |
Article |
| author |
Gardezi, S.J.S. Faye, I. Adjed, F. Kamel, N. Eltoukhy, M.M. |
| spellingShingle |
Gardezi, S.J.S. Faye, I. Adjed, F. Kamel, N. Eltoukhy, M.M. Mammogram classification using curvelet GLCM texture features and GIST features |
| author_sort |
Gardezi, S.J.S. |
| title |
Mammogram classification using curvelet GLCM texture features and GIST features |
| title_short |
Mammogram classification using curvelet GLCM texture features and GIST features |
| title_full |
Mammogram classification using curvelet GLCM texture features and GIST features |
| title_fullStr |
Mammogram classification using curvelet GLCM texture features and GIST features |
| title_full_unstemmed |
Mammogram classification using curvelet GLCM texture features and GIST features |
| title_sort |
mammogram classification using curvelet glcm texture features and gist features |
| publisher |
Springer Verlag |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994508050&doi=10.1007%2f978-3-319-48308-5_67&partnerID=40&md5=be837cf29475b0685f78e75292854546 http://eprints.utp.edu.my/20333/ |
| _version_ |
1741196354860875776 |
| score |
11.62408 |