Abstract
This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.
Original language | English |
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Title of host publication | Unknown Host Publication |
Publisher | Springer |
Pages | 463-470 |
Number of pages | 8 |
Volume | 10317 |
ISBN (Print) | 978-3-319-59876-5 |
DOIs | |
Publication status | Published online - 2 Jun 2017 |
Event | Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017 - Montreal, Canada Duration: 2 Jun 2017 → … |
Conference
Conference | Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017 |
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Period | 2/06/17 → … |
Keywords
- Mammography
- breast density
- local ternary patterns
- classification