Name
Abstract | ||
|---|---|---|
Mammogram tissue density has been found to be a strong indicator for breast cancer risk. Efforts in computer vision of breast parenchymal pattern have been made in order to improve the diagnostic accuracy by radiologists. Motivated by recent results in mammogram tissue density classification, a novel methodology for automatic American College of Radiology Breast Imaging Reporting and Data System classification using local binary pattern variance descriptor is presented in this article. The proposed approach characterizes the local density in different types of breast tissue patterns information into the LBP histogram. The performance of macro-calcification detection methods is developed using FARABI database. Performance results are given in terms of receiver operating characteristic. The area under curve of the corresponding approach has been found to be 79%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1687-5281-2013-19 | EURASIP J. Image and Video Processing |
Keywords | Field | DocType |
Mammogram, Breast tissue, Texture, Classification, Feature extraction, Macro-calcification detection, LBP, LBPV, ROC, ACR/BIRADS, CAD, ANN | Local binary pattern variance,Histogram,Computer vision,Receiver operating characteristic,Pattern recognition,Breast cancer,Computer science,Breast imaging,Feature extraction,Artificial intelligence,Biometrics | Journal |
Volume | Issue | ISSN |
2013 | 1 | 1687-5281 |
Citations | PageRank | References |
4 | 0.36 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alima Dammak Masmoudi | 1 | 8 | 0.79 |
| Norhene Gargouri Ben Ayed | 2 | 4 | 0.36 |
| Dorra Sellami Masmoudi | 3 | 42 | 8.85 |
| Riad Abid | 4 | 4 | 0.36 |