Author/Authors :
Quintanilla-Domingue, J. Technical University of Madrid - Group for Automation in Signals and Communications GASC, Spain , Quintanilla-Domingue, J. University of Guanajuato - Computational Intelligence Laboratory LABINCO-DICIS, Mexico , Ojeda-Magan, B. Technical University of Madrid - Group for Automation in Signals and Communications GASC, Spain , Ojeda-Magan, B. University of Guadalajara - Department of Project Engineering CUCEI, Mexico , Cortina-Januchs, M.G. Technical University of Madrid - Group for Automation in Signals and Communications GASC, Spain , Cortina-Januchs, M.G. University of Guanajuato - Computational Intelligence Laboratory LABINCO-DICIS, Mexico , Ruelas, R. University of Guadalajara - Department of Project Engineering CUCEI, Mexico , Vega-Corona, A. University of Guanajuato - Computational Intelligence Laboratory LABINCO-DICIS, Mexico , Andinaa, D. Technical University of Madrid - Group for Automation in Signals and Communications GASC, Spain
Abstract :
Breast cancer is one of the leading causes of female mortality in the world, and early detection is an important means of reducing the mortality rate. The presence of microcalcification 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 effective approach, in order to detect microcalcification clusters in digitized mammograms, based on the synergy of image processing and partitional (hard and fuzzy) clustering techniques. Mathematical morphology has been used for image processing, and is used in this work as a first step, with the purpose of enhancing the contrast of microcalcifications. Image segmentation is an important task in the field of image processing, in order to identify regions with the same features. In the second step, we use image segmentation, using three partitional, hard and fuzzy clustering algorithms, such as k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means, in order to make a comparison of the advantages and drawbacks offered by these algorithms, and which should help to improve the detection of microcalcification clusters in digitized mammograms.
