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@InProceedings{ZafalonKovacsFerr:2017:De3DSa,
               author = "Zafalon Kovacs, Nicole and Ferrari, Ricardo Jos{\'e}",
          affiliation = "{Universidade Federal de S{\~a}o Carlos} and {Universidade 
                         Federal de S{\~a}o Carlos}",
                title = "Detection of 3D salient points in magnetic resonance images using 
                         the dual-tree complex wavelet transform",
            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 = "3D salient points, DT-CWT, MRI, complex wavelets.",
             abstract = "Detection of 3D salient points in medical images has many 
                         important applications such as image registration and mesh 
                         positioning for the purpose of segmentation of important 
                         anatomical structures. In this study, we present preliminary 
                         results of a proposed method for the detection of 3D salient 
                         points in Magnetic Resonance (MR) images of human brain. Our 
                         method, which is based on the Dual-Tree Complex Wavelet Transform, 
                         combines the oriented wavelet sub-bands (by multiplying the ones 
                         on the same scale and upsampling the result to an upper level 
                         scale) to create an image map in which local maxima correspond to 
                         the salient points. Qualitative assessment was conducted using 566 
                         brain MRI images, whose results were combined together to create a 
                         point cloud showing the concentration of the salient points on 
                         important brain regions. The results indicate that our method has 
                         a great potential to detect important 3D salient points in MR 
                         images.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJN3PE",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJN3PE",
           targetfile = "artigo.pdf",
        urlaccessdate = "2021, Mar. 02"
}


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