@article{scholars1279,
           title = {A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram},
            note = {cited By 120},
          volume = {40},
          number = {4},
             doi = {10.1016/j.compbiomed.2010.02.002},
         journal = {Computers in Biology and Medicine},
           pages = {384--391},
            year = {2010},
        abstract = {This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2{\~A}?5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant. {\^A}{\copyright} 2010 Elsevier Ltd.},
        keywords = {Breast Cancer; Curvelets; Digital mammogram; Digital mammograms; Multiresolution wavelets, Classifiers; Frequency bands; Mammography; Wavelet transforms; X ray screens, Feature extraction, article; benign tumor; breast cancer; cancer diagnosis; controlled study; decomposition; digital mammography; image analysis; malignant neoplastic disease; priority journal; statistical analysis, Algorithms; Breast Neoplasms; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Mammography, Euclidia},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950859535&doi=10.1016\%2fj.compbiomed.2010.02.002&partnerID=40&md5=7e4164888f0ac58310de30aa2eab81f1},
            issn = {00104825},
          author = {Meselhy Eltoukhy, M. and Faye, I. and Belhaouari Samir, B.}
}