Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection
Corresponding email: anggrek.citra.n@gmail.com
Published at : 30 Jan 2016
Volume : IJtech
Vol 7, No 1 (2016)
DOI : https://doi.org/10.14716/ijtech.v7i1.1393
Nusantara, A.C., Purwanti, E., Soelistiono, S., 2016. Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection. International Journal of Technology. Volume 7(1), pp. 71-77
| Anggrek Citra Nusantara | Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Kampus C Universitas Airlangga, Surabaya 60115, Indonesia |
| Endah Purwanti | Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Kampus C Universitas Airlangga, Surabaya 60115, Indonesia |
| Soegianto Soelistiono | Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Kampus C Universitas Airlangga, Surabaya 60115, Indonesia |
Breast cancer can be detected using digital mammograms. In this research study, a system is designed to classify digital mammograms into two classes, namely normal and abnormal, using the k-Nearest Neighbor (kNN) method. Prior to classification, the region of interest (ROI) of a mammogram is cropped, and the feature is extracted using the wavelet transformation method. Energy, mean, and standard deviation from wavelet decomposition coefficients are used as input for the classification. Optimal accuracy is obtained when wavelet decomposition level 3 is used with the feature combination of mean and standard deviation. The highest accuracy, sensitivity, and specificity of this method are 96.8%, 100%, and 95%, respectively.
Breast cancer, k-Nearest Neighbor, Mammogram, Wavelet transformation
American Cancer Society, 2014. Detailed Guide: Breast Cancer. Available online at: http://www.cancer.org/cancer/breastcancer/detailedguide/, Accessed on 12 November 2014
Baratloo, A., Hosseini, M., Negida, A., Ashal, G.E., 2015. Simple Definition and Calculation of Accuracy, Sensitivity and Specificity. Emergency, Volume 3(2), pp. 48-49
Birdwell, R.L., Ikeda, D.M., O’Shaughnessy, K.F., Sickles, E.A., 2001. Mammographic Characteristics of 115 Missed Cancers Later Detected with Screening Mammography and the Potential Utility of Computer-aided Detection. Radiology, Volume 219(1), pp. 192-202
Duda, R.O., Hart, P.E., Stork, D.G., 2000. Pattern Classification (2nd edition), Wiley-Interscience, USA
Ferreira, C.B.R., Borges, D.L., 2001. Automated Mammogram Classification using a Multi-resolution Pattern Recognition Approach. IEEE, USA
Globocan, 2012. Fact Sheets Population Estimated Cancer Incidence Mortality and Prevalence Worldwide. Available online at: http://globocan.iarc.fr/Pages/fact_sheets_population.aspx, Accessed on 11 November 2014
Hamad, N.B., Taouil, K., Bouhlel, M.S., 2013. Mammographic Microcalcification Detection using Discrete Wavelet Transform. International Journal of Computer Applications, Volume 64(21), pp. 17-22
Isource National Breast Cancer Centre, 2011. Clinical Practice Guidelines for the Management of Early Breast Cancer, National Health & Medical Research Council, Australia
Karahaliou, A.N., Boniatis, I.S., Skiadopoulos, S.G., Sakellaropoulos, F.N., Likaki, E., Panayiotakis, G.S., Costaridou, L.I., 2006. A Texture Analysis Approach for Characterizing Microcalcifications on Mammograms. In: the
Proceeding of IEEE International Special Topic Conference on Information Technology Applications in Biomedicine 2006, 26-28 October, Greece
Lowis, X., Hendra, Y., Lavinia, Z., 2015. The Use of Dual-tree Complex Wavelet Transform (DTCWT) Based Feature for Mammogram Classification. International Journal of Signal Processing, Image Processing, and Pattern Recognition, Volume 8(3), pp. 87-96
Michell, M., 2010. Breast Cancer: Contemporary Issues in Cancer Imaging, Cambrigde University Press, UK
Mustra, M., Grgic, M., Delac, K., 2010. Feature selection for automatic breast density classification. In: the Proceedings of the 52nd International Symposium Elmar 2010, 15-17 September, Croatia
Prathibha, B.N., Sadasivam, V., 2010. Multi-resolution Texture Analysis of Mammograms using Nearest Neighbor Classification Techniques. International Journal of Information Acquisition, Volume 7(2), pp. 109-118
Putra, D., 2010. Pengolahan Citra Digital, Penerbit Andi, Yogyakarta. (in Bahasa)
Radiologyinfo, 2014. Mammography, Available online at: www.radiologyinfo.org/en/info.cfm?pg=mammo, Accessed on 12 November 2014
Suckling, J., Parker, J., Dance, D.R., Astley, S., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S-L, Taylor, P., Betal, D., Savage, J., 1994, Digital Mammography, pp. 375-378