Abstract
A supervised hierarchical classification for mammographic images is here presented, based on the analysis of wavelet transform. The proposed method analyses different wavelet transform decomposition levels at the same time and innovatively treats them as new images taking into account also their spatial, contextual and morphological properties. The proposed method is very simple and transparent to the user, but it is able to achieve very good classification results. Since it is very fast, we foresee its future application not only for classification purposes, but also for pathology automatic localization. MIAS database has been used in order to compare results with the ones presented in literature.
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References
Strickland, N., Hahn, H.I.: Wavelet Transforms for Detecting Microcalcification in Mammograms. IEEE Transactions on Medical Imaging 15, 218–229 (1996)
Zhang, W., Yoshida, H., Nishikawa, R.M., Doi, K.: Optimally Weighted Wavelet Transform Based on Supervised Training for Detection of Microcalcifications in Digital Mammograms. Medical Physics 25, 949–956 (1998)
Bagci, A.M., Yardimci, Y., Çetin, A.E.: Detection of Microcalcification Clusters in Mammogram Images Using Local Maxima and Adaptive Wavelet Transform Analysis. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. 4, pp. 3856–3859 (2002)
Petrosian, A., Chan, H.-P., Helvie, M.A., Goodsitt, M.M., Adler, D.D.: Computer-Aided Diagnosis in Mammography: Classification of Mass and Normal Tissue by Texture Analysis. Phys. Med. Biol. (39), 2273–2288 (1994)
Chiracharit, W., Sun, Y., Kumhom, P., Chamnongthai, K., Babbs, C., Delp, E.J.: Normal Mammogram Classification Based on a Support Vector Machine Utilizing Crossed Distribution Features. In: Proc. of the 26th Int. Conf. IEEE EMBS, vol. 1, pp. 1581–1584 (2004)
Zaïane, O.R., Antonie, M.L., Coman, A.: Mammography Classification by an Association Rule-based Classifier. In: Int. Workshop on Multimedia Data Mining, pp. 62–69 (2002)
Suckling, J., Parker, J., et al.: The Mammographic Images Analysis Society digital mammogram database. In: Gale, A.G., Astley, S.M., et al. (eds.) Digital Mammography. Exerpta Medica International Congress Series, vol. 1069, pp. 375–378. Elsevier, Amsterdam (1994), http://www.wiau.man.ac.uk/services/MIAS/
Ferreira, C.B.R., Borges, D.L.: Analysis of Mammogram Classification Using a Wavelet Transform Decomposition. Pattern Recognition Letters (24), 973–982 (2003)
Mousa, R., Munib, Q., Moussa, A.: Breast Cancer Diagnosis System Based on Wavelet Analysis and Fuzzy-Neural. Expert Systems with Applications 28(4), 713–723 (2005)
Yang, J.C., Shin, J.W., Park, D.S.: Comparing Study for Detecting Microcalcifications in Digital Mammogram Using Wavelets. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 409–415. Springer, Heidelberg (2004)
Soltanian, H., Zadeh, F., Rafiee Rad, S., Pourabdollah-Nejad, D.: Comparison of Multiwavelet, Wavelet, Haralick, and Shape Features for Microcalcification Classification in Mammograms. Pattern Recognition (37), 1973–1986 (2004)
Bruce, L.M., Adhami, R.R.: Classifying Mammographic Mass Shapes Using Wavelet Transform Modulus-Maxima Method. IEEE Trans. On Medical Imaging 18(12), 1170–1177 (1999)
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Dellepiane, S.G., Minetti, I., Dellepiane, S. (2010). A Hierarchical Classification Method for Mammographic Lesions Using Wavelet Transform and Spatial Features. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_37
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DOI: https://doi.org/10.1007/978-3-642-15910-7_37
Publisher Name: Springer, Berlin, Heidelberg
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