Abstract
Classification of benign and malignant masses in mammograms is a complex task due to the appearance similarities in both classes. Thus, classification of masses in mammograms is considered an important step in the development of current Computer Aided Diagnosis (CAD) systems. In this paper, we present a way to classify masses without the need for segmentation. A supervised texton-based approach is developed using filter bank responses. Subsequently, a Support Vector Machine (SVM) classifier is used to classify the images. We evaluated the results on a subset of publicly available dataset (DDSM) and obtained classification accuracy of 96% which is comparable to the state-of-the-art techniques developed for the task of mammographic mass classification.
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References
Breast Imaging Reporting and Data System atlas (BI-RADS atlas). Reston, VA: American College of Radiology 98 (2003)
Buciu, I., Gacsadi, A.: Directional features for automatic tumor classification of mammogram images. Biomed. Sig. Process. Control 6(4), 370–378 (2011)
Chen, Z., Denton, E., Zwiggelaar, R.: Modelling breast tissue in mammograms for mammographic risk assessment. In: MIUA, pp. 37–42 (2011)
Chen, Z., Oliver, A., Denton, E., Boggis, C., Zwiggelaar, R.: Classification of microcalcification clusters using topological structure features. In: Medical Image Understanding and Analysis, pp. 37–42 (2012)
Chen, Z., Strange, H., Oliver, A., Denton, E., Boggis, C., Zwiggelaar, R.: Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans. Biomed. Eng. 62(4), 1203–1214 (2015)
Djaroudib, K., Ahmed, A., Zidani, A.: Textural approach for mass abnormality segmentation in mammographic images. arXiv preprint arXiv:1412.1506 (2014)
Ertas, G., Gulcur, H., Aribal, E., Semiz, A.: Feature extraction from mammographic mass shapes and development of a mammogram database. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 2752–2755. IEEE (2001)
Esener, İ.I., Ergin, S., Yüksel, T.: A new ensemble of features for breast cancer diagnosis. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1168–1173. IEEE (2015)
Feig, S., Shaber, G., Schwartz, G., Patchefsky, A., Libshitz, H., Edeiken, J., Nerlinger, R., Curley, R., Wallace, J.: Thermography, mammography, and clinical examination in breast cancer screening: review of 16,000 studies 1. Radiology 122(1), 123–127 (1977)
Ferlay, J., Héry, A., Autier, P., Sankaranarayanan, R.: Global burden of breast cancer. In: Li, C. (ed.) Breast Cancer Epidemiol., pp. 1–19. Springer, Heidelberg (2010). doi:10.1007/978-1-4419-0685-4_1
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001)
Huo, Z., Giger, M., Vyborny, C., Wolverton, D., Metz, C.: Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. Acad. Radiol. 7(12), 1077–1084 (2000)
Li, Y., Chen, H., Rohde, G., Yao, C., Cheng, L.: Texton analysis for mass classification in mammograms. Pattern Recogn. Lett. 52, 87–93 (2015)
Lissner, J., Kessler, M., Anhalt, G., Hahn, D., Wendt, T., Seiderer, M.: Developments in methods for early detection of breast cancer. In: Zander, J., Baltzer, J. (eds.) Early Breast Cancer, pp. 93–112. Springer, Heidelberg (1985). doi:10.1007/978-3-642-70192-4_11
Maass, H.: Identification of a high-risk population. In: Zander, J., Baltzer, J. (eds.) Early Breast Cancer, pp. 87–92. Springer, Heidelberg (1985). doi:10.1007/978-3-642-70192-4_10
Rampun, A., Tiddeman, B., Zwiggelaar, R., Malcolm, P.: Computer aided diagnosis of prostate cancer: a texton based approach. Med. Phys. 43(10), 5412–5425 (2016)
Rangayyan, R., El-Faramawy, N., Desautels, J., Alim, O.: Measures of acutance and shape for classification of breast tumors. IEEE Trans. Med. Imaging 16(6), 799–810 (1997)
Sahakyan, A., Sarukhanyan, H.: Segmentation of the breast region in digital mammograms and detection of masses. Int. J. Adv. Comput. Sci. Appl. 3(2) (2012)
Serifovic-Trbalic, A., Trbalic, A., Demirovic, D., Prljaca, N., Cattin, P.: Classification of benign and malignant masses in breast mammograms. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 228–233. IEEE (2014)
Strange, H., Chen, Z., Denton, E.E., Zwiggelaar, R.: Modelling mammographic microcalcification clusters using persistent mereotopology. Pattern Recogn. Lett. 47, 157–163 (2014)
Suhail, Z., Sarwar, M., Murtaza, K.: Automatic detection of abnormalities in mammograms. BMC Med. Imaging 15(1), 53 (2015)
Vaidehi, K., Subashini, T.: Automatic characterization of benign and malignant masses in mammography. Procedia Comput. Sci. 46, 1762–1769 (2015)
Valarmathie, P., Sivakrithika, V., Dinakaran, K.: Classification of mammogram masses using selected texture, shape and margin features with multilayer perceptron classifier. Biomed. Res. (2016)
Varma, M., Zisserman, A.: Texture classification: are filter banks necessary? In: Proceedings of 2003 IEEE computer society conference on Computer vision and pattern recognition, vol. 2, pp. II-691. IEEE (2003)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1–2), 61–81 (2005)
Zhang, L., Fisher, M., Wang, W.: Retinal vessel segmentation using gabor filter and textons. Med. Image Underst. Anal. (MIUA 2014), 155–160 (2014)
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Suhail, Z., Hamidinekoo, A., Denton, E.R.E., Zwiggelaar, R. (2017). A Texton-Based Approach for the Classification of Benign and Malignant Masses in Mammograms. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_31
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