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

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

Description

Title: Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms
Author/s:
  • Quintanilla Domínguez, Joel
  • Cortina Januchs, María Guadalupe
  • Ojeda Magaña, Benjamín
  • Jevtić, Aleksandar
  • Vega Corona, Antonio
  • Andina de la Fuente, Diego
Item Type: Presentation at Congress or Conference (Article)
Event Title: World Automation Congress, WAC2010
Event Dates: 19/09/2010 - 23/09/2010
Event Location: Kobe, Japón
Title of Book: Proceedings of the World Automation Congress, WAC2010
Date: 2010
ISBN: 978-1-4244-9673-0
Subjects:
Freetext Keywords: Microcalcifications Clusters, Mathematical Morphology, Artificial Neural Networks, Pattern Recognition.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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

More information

Item ID: 8112
DC Identifier: http://oa.upm.es/8112/
OAI Identifier: oai:oa.upm.es:8112
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665695&tag=1
Deposited by: Memoria Investigacion
Deposited on: 17 Aug 2011 11:20
Last Modified: 20 Apr 2016 17:01
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