dc.contributorQuintanilla-Domínguez, J., Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain, Technological University of the Southwest of Guanajuato, Guanajuato, Mexico; Ojeda-Magaña, B., Department of Projects Engineering DIP-CUCEI, University of Guadalajara, Guadalajara, Mexico; Marcano-Cedeño, A., Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain; Barrón-Adame, J.M., Technological University of the Southwest of Guanajuato, Guanajuato, Mexico; Vega-Corona, A., Computational Intelligence Laboratory LABINCO-DICIS, University of Guanajuato, Guanajuato, Mexico; Andina, D., Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain
dc.creatorQuintanilla-Dominguez, J.
dc.creatorOjeda-Magana, B.
dc.creatorMarcano-Cedeno, A.
dc.creatorBarron-Adame, J.M.
dc.creatorVega-Corona, A.
dc.creatorAndina, D.
dc.date.accessioned2015-11-19T18:52:23Z
dc.date.accessioned2023-07-03T22:38:40Z
dc.date.available2015-11-19T18:52:23Z
dc.date.available2023-07-03T22:38:40Z
dc.date.created2015-11-19T18:52:23Z
dc.date.issued2013
dc.identifierhttp://hdl.handle.net/20.500.12104/67672
dc.identifier10.1080/1931308X.2013.838070
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84889668382&partnerID=40&md5=1b1e894a4b1e73f7867800895d6d1650
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7246247
dc.description.abstractThis paper presents a novel method for the automatic detection of microcalcifications in regions of interest images. Automatic detection method is implemented by feature extraction and sub-segmentation steps. The feature extraction step is improved using a top-hat transform such that microcalcifications can be highlighted. In a second step a sub-segmentation method based on the possibilistic fuzzy c-means clustering algorithm is applied in order to segment the images and as a way to identify the atypical pixels inside the regions of interest as the pixels representing microcalcifications. Once the pixels representing these objects have been identified, an artificial neural network model is used to learn the relations between atypical pixels and microcalcifications, such that the model can be used for aid diagnosis, and a medical could determine if these regions of interest are benign or malignant. So, as the results show, the proposed approach is a good alternative for the detection of suspicious regions, which could be of great help for medical diagnosis. © 2013, TSI® Press.
dc.relationInternational Journal of Intelligent Computing in Medical Sciences and Image Processing
dc.relation5
dc.relation2
dc.relation161
dc.relation174
dc.relationScopus
dc.titleAutomatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN
dc.typeArticle


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