Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks
Data(s) |
2011
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Resumo |
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 |
Formato |
application/pdf |
Identificador | |
Idioma(s) |
spa |
Publicador |
E.U.I.T. Telecomunicación (UPM) |
Relação |
http://oa.upm.es/12258/1/INVE_MEM_2011_96007.pdf http://asp.eurasipjournals.com/content/2011/1/91/ info:eu-repo/semantics/altIdentifier/doi/10.1186/1687-6180-2011-91 |
Direitos |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess |
Fonte |
EURASIP journal on advances in signal processing, ISSN 1687-6172, 2011, No. 91 |
Palavras-Chave | #Telecomunicaciones #Informática |
Tipo |
info:eu-repo/semantics/article Artículo PeerReviewed |