Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Quintanilla Domínguez, Joel; Cortina Januchs, María Guadalupe; Ojeda Magaña, Benjamín; Jevtić, Aleksandar; Vega Corona, Antonio y Andina de la Fuente, Diego (2010). Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms . En: "World Automation Congress, WAC2010", 19/09/2010 - 23/09/2010, Kobe, Japón. ISBN 978-1-4244-9673-0.

Descripción

Título: Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms
Autor/es:
  • Quintanilla Domínguez, Joel
  • Cortina Januchs, María Guadalupe
  • Ojeda Magaña, Benjamín
  • Jevtić, Aleksandar
  • Vega Corona, Antonio
  • Andina de la Fuente, Diego
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: World Automation Congress, WAC2010
Fechas del Evento: 19/09/2010 - 23/09/2010
Lugar del Evento: Kobe, Japón
Título del Libro: Proceedings of the World Automation Congress, WAC2010
Fecha: 2010
ISBN: 978-1-4244-9673-0
Materias:
Palabras Clave Informales: Microcalcifications Clusters, Mathematical Morphology, Artificial Neural Networks, Pattern Recognition.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.9875

Más información

ID de Registro: 8112
Identificador DC: http://oa.upm.es/8112/
Identificador OAI: oai:oa.upm.es:8112
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665695&tag=1
Depositado por: Memoria Investigacion
Depositado el: 17 Ago 2011 11:20
Ultima Modificación: 20 Abr 2016 17:01
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