Abstract
We propose a two steps method for the automatic classifi- cation of microcalcifications in Mammograms. The first step performs the improvement of the visualization of any abnormal lesion through feature enhancement based in multiscale wavelet representations of the mammographic images. In a second step the automatic recognition of microcalcifications is achieved by the application of a Neural Network optimized in the Neyman-Pearson sense. That means that the Neural Network presents a controlled and very low probability of classifying abnormal images as normal.
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Feig, S.A., Hendrick, R.E.: Risk, Benefit and Controversy in Mammographic Screening. In A Categorical Course in Physics. A.G. Haus and M.J. Yaffe. (eds). Radiological Society of North America. (1993) 119–135
Feig, S.A.: Mammogramphic Evaluation of Calcifications. In A Categorical Course in Breast Imaging. (eds). Radiological Society of North America. (1995) 93–105
Bassett, L.W.: Mammographic Analysis of Calcifications. Radiologic Clinics of North America. 30 (1992) 93–105
Strickland, R.N., Hahn, H.I.: Wavelet Transform for Detecting Microcalcifications in Mammograms. IEEE Trans. on Medical Imaging, Vol. 15. 2 (1996) 218–229
Jeine, J., Deans, S.R.: Multirresolution Statistical Analysis of High-Resolution Digital Mammograms. IEEE Trans. on Medical Imaging. Vol. 16. 5 (1997) 503–515
Redner, R., Walker, H.: Mixture Densities, Maximum Likelihood and the EM Elgorithm. SIAM Review. 26 (1994) 195–239
Mallat, S., Zhong, S.: Characterization of Signals from Multiscale Edge. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 14. 7 (1992) 710–732
Sanz-González, J.L., Andina, D.: Performance Analysis of Neural Network Detectors by Importance Sampling Techniques. Neural Processing Letters. Kluwer Academic Publishers. Netherlands. ISSN 1370-4621, Vol.9. 3 (1999) 257–269
Feig, S.A., Shaber, G.S., Patchefsky, A.: Analysis of Clinically Occult and Mammographically Occult Breast Tumors. American Journal of Radiology, Vol. 128. (1997) 403–408
Meyer, Y.: Onddelettes et Operateurs. Herman, New York. (1990)
Mu, Y., Giger M.L., Doi, K.: Artificial Network in Mammography. Application to Decision Making in the Diagnosis of Breast Cancer. Radiology, Vol. 187. (1993) 81–87
Qian, W., Clarke, L.P., Kallergi, M., Clark, R.: Tree-Structured Nonlinear Filters in Digital Mammography. IEEE Trans. on Medical Imaging, Vol. 13. 1 (1994) 25–36
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Andina, D., Vega-Corona, A. (2001). Detection of Microcalcifications in Mammograms by the Combination of a Neural Detector and Multiscale Feature Enhancement. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_46
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DOI: https://doi.org/10.1007/3-540-45723-2_46
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