@InProceedings{OliveiraSanMelRêgBat:2016:InThDe,
               author = "Oliveira, Hugo Neves de and Santos, Jefersson Alex dos and Melo, 
                         Matheus Cordeiro de and R{\^e}go, Tha{\'{\i}}s Gaudencio do and 
                         Batista, Leonardo Vidal",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal da Para{\'{\i}}ba} 
                         and {Universidade Federal da Para{\'{\i}}ba} and {Universidade 
                         Federal da Para{\'{\i}}ba}",
                title = "Information Theory-based Detection of Noisy Bit Planes in Medical 
                         Images",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "noise detection, mammogram classification, information theory, 
                         data compression.",
             abstract = "Mammographic Computer-Aided Diagnosis systems are applications 
                         designed to assist radiologists in diagnosis of malignancy in 
                         mammographic findings. Most methods described in the literature do 
                         not perform a proper preprocessing step in mammographic images 
                         prior to classification, which can generate inconsistent results 
                         due to the potentially large amount of noise in medical images. 
                         This paper proposes a new method based on Information Theory and 
                         Data Compression for detection of random noise in image bit 
                         planes. In order to validate the efficiency of the proposed noise 
                         removal method, we used Machine Learning algorithms to classify 
                         mammographic findings from the Digital Database for Screening 
                         Mammography. Results using texture features indicate that a 
                         reduction in the radiometric resolution of 4 or 5 bit planes in 
                         digitized screen film mammographic images result in a better 
                         classification performance.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.014",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.014",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5GCSH",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5GCSH",
           targetfile = "
                         
                         Information_Theory_based_Detection_of_Noisy_BitPlanes_in_Medical_Images_Final.pdf",
        urlaccessdate = "2024, May 02"
}