@InProceedings{ColellaRitt:2017:SeLuLe,
               author = "Colella, S{\'{\i}}lvia Regina Leme and Rittner, 
                         Let{\'{\i}}cia",
          affiliation = "{University of Campinas - UNICAMP} and {University of Campinas - 
                         UNICAMP}",
                title = "Segmentation of lung and its lesions in computer tomographic 
                         images",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "medical image segmentation, interstitial lung diseases, computer 
                         tomography.",
             abstract = "The purpose of this work is to propose two new automatic 
                         segmentation methods in CT images: one for the lungs and one for 
                         their lesions. The lung segmentation method uses morphological 
                         filters and the max-tree, a data structure that represents an 
                         image through its connected components. Results show that the 
                         method presented a good performance when compared to the manual 
                         segmentation and it was able to not exclude lesions located in the 
                         borders in most of the images, which is challenging when the 
                         lesions are small and disconnected located in this region. This 
                         method obtained an average Dice of 98%. The lesion segmentation 
                         method uses the image with the segmented lungs to calculate the 
                         features to train a classifier that distinguishes between normal 
                         tissue and abnormal tissue (which contains lesions). This method 
                         also presented good results as it turned out not being very 
                         sensible to parameters' choice and it obtained an average Dice of 
                         62% for the slices with severe pathologies.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJ56FE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PJ56FE",
           targetfile = "wtd-sibgrapi-2017-SilviaColella-camera-ready.pdf",
        urlaccessdate = "2024, Apr. 29"
}