Abstract
In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of statistical descriptors, based on Independent Component Analysis (ICA), derive the source regions that generate the observed ROS in mammograms. The reduced set of linear transformation coefficients, estimated from ICA after principal component analysis (PCA), compose the features vector that describes the observed regions in an effective way. The ROS are diagnosed using support-vector-machines (SVMs) with polynomial and radial basis function kernels. Taking into account the small number of training data, the PCA preprocessing step reduces the dimensionality of the features vector and consequently improves the classification accuracy of the SVM classifier. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have given high recognition accuracy above 87%.
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References
Martin, J., Moskowitz, M., Milbrath, J.: Breast cancer missed by mammography. AJR 132, 737 (1979)
Kalisher, L.: Factors influencing false negative rates in xero-mammography. Radiology 133, 297 (1979)
Tabar, L., Dean, B.P.: Teaching Atlas of Mammography, 2nd edn., Thieme, NY (1985)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Fast Detection of Masses in Computer- Aided Mammography. IEEE Signal Processing Magazine 17(1), 54–64 (2000)
Sonka, M., Fitzpatrick, J.: Handbook of Medical Imaging. SPIE Press (2000)
Doi, K., Giger, M., Nishikawa, R., Schmidt, R. (eds.): Digital Mammography 1996. Elsevier, Amsterdam (1996)
Christoyianni, I., Koutras, A., Dermatas, E., Kokkinakis, G.: Computer Aided Diagnosis of Breast Cancer in Digitized Mammograms. Computerized Medical Imaging and Graphics 26(5), 309–319 (2002)
Bazzani, A., Bevilacqua, A., Bollini, D., Brancaccio, R., Campanini, R., Lanconelli, N., Riccardi, A., Romani, D., Zamboni, G.: Automatic detection of clustered microcalcifications in digital mammograms using a SVM classifier. In: European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 195–200 (2000)
Wei, L., Yang, Y., Nishikawa, R.M., Jiang, Y.: A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications. IEEE Transactions on Medical Imaging, 1–10 (January 2005)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Neural Classification of Abnormal Tissue in Digital Mammography Using Statistical Features of the Texture. IEEE Int. Conference on Electronics, Circuits and Systems 1, 117–120 (1999)
Schφlkopf, B., Burges, C.J.C., Smola, A.J.: Advances in Kernel Methods. In: Support Vector Learning, The MIT Press, London (1999)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data mining and Knowledge Dicovery 2, 121–167 (1998)
Lee, T.-W.: Independent Component Analysis: Theory and Applications. Kluwer Academic Publishers, Dordrecht (1998)
Bartlett, M., Lades, M., Sejnowski, T.: Independent component representation for face recognition. In: Proc. SPIE Symposium on Electronic Imaging: Science and Technology (1998)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewoo Cliffs (1999)
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Koutras, A., Christoyianni, I., Georgoulas, G., Dermatas, E. (2006). Computer Aided Classification of Mammographic Tissue Using Independent Component Analysis and Support Vector Machines. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_59
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DOI: https://doi.org/10.1007/11840930_59
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