Abstract
This paper presents and tests a methodology that sinergically combines a select of successful advances in each step to automatically classify microcalcifications (MCs) in digitized mammography. The method combines selection of regions of interest (ROI), enhancement by histogram adaptive techniques, processing by multiscale wavelet and gray level statistical techniques, generation, clustering and labelling of suboptimal feature vectors (SFVs), and a Neural feature selector and detector to finally classify the MCs. The experimental results with the method promise interesting advances in the problem of automatic detection and classification of MCs1.
This research has been supported by the National Spanish Research Institution “Comisin Interministerial de Ciencia y Tecnologa-CICYT” as part of the project TIC2002-03519
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Vega-Corona, A., Álvarez-Vellisco, A., Andina, D. (2003). Feature Vectors Generation for Detection of Microcalcifications in Digitized Mammography Using Neural Networks. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_74
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DOI: https://doi.org/10.1007/3-540-44869-1_74
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