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. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.20333 / |
| Published: |
Springer Verlag
2017
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| Online Access: |
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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