Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

Quintanilla Domínguez, Joel; Ojeda Magaña, Benjamín; Marcano Cedeño, Alexis Enrique; Cortina Januchs, María Guadalupe y Andina de la Fuente, Diego (2011). Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks. "EURASIP journal on advances in signal processing" (n. 91); pp. 1-11. ISSN 1687-6172. https://doi.org/10.1186/1687-6180-2011-91.

Descripción

Título: Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks
Autor/es:
  • Quintanilla Domínguez, Joel
  • Ojeda Magaña, Benjamín
  • Marcano Cedeño, Alexis Enrique
  • Cortina Januchs, María Guadalupe
  • Andina de la Fuente, Diego
Tipo de Documento: Artículo
Título de Revista/Publicación: EURASIP journal on advances in signal processing
Fecha: 2011
Materias:
Escuela: E.U.I.T. Telecomunicación (UPM) [antigua denominación]
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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

Más información

ID de Registro: 12258
Identificador DC: http://oa.upm.es/12258/
Identificador OAI: oai:oa.upm.es:12258
Identificador DOI: 10.1186/1687-6180-2011-91
URL Oficial: http://asp.eurasipjournals.com/content/2011/1/91/
Depositado por: Memoria Investigacion
Depositado el: 14 Ago 2012 11:05
Ultima Modificación: 21 Abr 2016 11:29
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