Feature Vectors Generation for Detection of Microcalcifications in Digitized Mammography Using Neural Networks

Authors:
Antonio Vega-Corona;Antonio Álvarez-Vellisco;Diego Andina
Affiliations:
F.I.M.E.E., Universidad de Guanajuato, Guanajuato, México;Departamentos SSR e ICS, Universidad Politécnica de Madrid, Madrid, Spain;Departamentos SSR e ICS, Universidad Politécnica de Madrid, Madrid, Spain
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Detection of Microcalcifications in Mammograms by the Combination of a Neural Detector and Multiscale Feature Enhancement

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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.