@InProceedings{RamirezVillegasLamERami:2009:MiDeMa,
               author = "Ramirez Villegas, Juan Felipe and Lam Espinosa, Eric and Ramirez 
                         Moreno, David Fernando",
          affiliation = "{Universidad Autonoma de Occidente} and {Universidad Autonoma de 
                         Occidente} and {Universidad Autonoma de Occidente}",
                title = "Microcalcification detection in mammograms using difference of 
                         gaussians filters and a hybrid feedforward-Kohonen neural 
                         network",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Microcalcification, mammogram, difference of gaussians filters, 
                         artificial neural networks, hard limit function, self-organizing 
                         map.",
             abstract = "This work develops a microcalcifications detection system in 
                         mammograms by using difference of Gaussians filters (DoG) and 
                         artificial neural networks (ANN). The digital image processing 
                         proposed show the basic wavelet-based behavior of DoG as a mother 
                         function frequently used in several vision tasks, and in this 
                         case, used in order to enhance the microcalcifications traces in 
                         standard mammograms and further to achieve its detection via ANN. 
                         In order to achieve this, a segmentation algorithm is implemented 
                         for reaching a threshold in already processed images, and finally, 
                         the resultant information is given to the ANN. The neural network 
                         used to perform the detection is a hybrid feedforward-Kohonen one, 
                         implemented with a hard-limit transfer function in the first layer 
                         and a self-organizing map (SOM) responsible for 
                         microcalcifications topologic adjustment in the second layer. 
                         Basically, this clustering method gave us a robust solution of the 
                         problem and the detection was performed efficiently. There are no 
                         considerations relative to morphologic analysis of 
                         microcalcifications for diagnosis in this work.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.25",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.25",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35S9PC8",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35S9PC8",
           targetfile = "PID949710.pdf",
        urlaccessdate = "2024, May 04"
}