International Science Index


An Approach Based on Statistics and Multi-Resolution Representation to Classify Mammograms


One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

[1] Malvezzi, M., Bertuccio, P., Levi, F., La Vecchia, C., & Negri, E. (2014). European cancer mortality predictions for the year 2014. Annals of Oncology, 25(8), 1650–1656.
[2] Pisano, E. D., Cole, E. B., Hemminger, B. M., Yaffe, M. J., Aylward, S. R., Maidment, A. D., ... & Fajardo, L. L. (2000). Image Processing Algorithms for Digital Mammography: A Pictorial Essay 1. Radiographics, 20(5), 1479–1491.
[3] Bozek, J., Mustra, M., Delac, K., & Grgic, M. (2009). A survey of image processing algorithms in digital mammography. In Recent advances in multimedia signal processing and communications (pp. 631-657). Springer Berlin Heidelberg.
[4] Moayedi, F., Azimifar, Z., Boostani, R., & Katebi, S. (2010). Contourlet-based mammography mass classification using the SVM family. Computers in biology and medicine, 40(4), 373-383.
[5] Jiang, J., Yao, B., & Wason, A. M. (2007). A genetic algorithm design for microcalcification detection and classification in digital mammograms. Computerized Medical Imaging and Graphics, 31(1), 49-61.
[6] Verma, B., McLeod, P., & Klevansky, A. (2009). A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern Recognition, 42(9), 1845-1852.
[7] Marrocco, C., Molinara, M., D’Elia, C., & Tortorella, F. (2010). A computer-aided detection system for clustered microcalcifications. Artificial intelligence in medicine, 50(1), 23-32.
[8] Khan, S., Hussain, M., Aboalsamh, H., & Bebis, G. (2015). A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimedia Tools and Applications, 1-25.
[9] Gedik, N. (2016). A new feature extraction method based on multi-resolution representations of mammograms. Applied Soft Computing, 44, 128-133.
[10] Guo, Y. N., Dong, M., Yang, Z., Gao, X., Wang, K., Luo, C., ... & Zhang, J. (2016). A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Computer Methods and Programs in Biomedicine, 130, 31-45.
[11] Gedik, N., & Atasoy, A. (2014). Performance evaluation of the wave atom algorithm to classify mammographic images. Turkish Journal of Electrical Engineering & Computer Sciences, 22(4), 957-969.
[12] Francis, S. V., Sasikala, M., & Saranya, S. (2014). Detection of breast abnormality from thermograms using curvelet transform based feature extraction. Journal of medical systems, 38(4), 1-9.
[13] Demanet, L., & Ying, L. (2007). Wave atoms and sparsity of oscillatory patterns. Applied and Computational Harmonic Analysis, 23(3), 368-387.
[14] Liu, H., Li, J., & Wong, L. (2002). A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome informatics, 13, 51-60.
[15] DDSM database. Database.html, (accessed 05.03.2016).