@InProceedings{SantosMasc:2018:StDiPa,
               author = "Santos, Cid Adinam Nogueira and Mascarenhas, Nelson Delfino 
                         D{\'A}vila",
          affiliation = "{Universidade Federal de S{\~a}o Carlos} and {Universidade 
                         Federal de S{\~a}o Carlos}",
                title = "Stochastic distances for patch-based ultrasound image 
                         despeckling",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "despeckling, ultrasound imaging, patch-based filtering, stochastic 
                         distances, geodesic distances, BM3D, NLM.",
             abstract = "Ultrasound image despeckling is an important research field since 
                         it can improve the interpretability of one of the main categories 
                         of medical imaging. Many techniques have been tried over the years 
                         for ultrasound despeckling, and more recently, a great deal of 
                         attention has been focused on patch-based methods, such as 
                         non-local means (NLM) and block-matching collaborative filtering 
                         (BM3D). A common idea in these recent methods is the measure of 
                         distance between patches, originally proposed as the Euclidean 
                         distance, for filtering additive white Gaussian noise. In this 
                         work, we derive several new similarity measures based on the 
                         statistics of the speckle and apply them for despeckling both 
                         radio frequency (RF) and log-compressed US signals. 
                         State-of-the-art results in filtering simulated, synthetic, and 
                         real ultrasound images confirm the potential of the proposed 
                         approach.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3U2NS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3S3U2NS",
           targetfile = "wtd_sibgrapi_2018_v2.pdf",
        urlaccessdate = "2024, May 19"
}