Author:
Quintanilla-Domínguez Joel,Ojeda-Magaña Benjamín,Marcano-Cedeño Alexis,Cortina-Januchs María G,Vega-Corona Antonio,Andina Diego
Abstract
Abstract
A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection.
Publisher
Springer Science and Business Media LLC
Cited by
10 articles.
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