@InProceedings{StringhiniWelfOrneGama:2019:LoCTDe,
               author = "Stringhini, Romulo Marconato and Welfer, Daniel and d'Ornellas, 
                         Marcos Cordeiro and Gamarra, Daniel Fernando Tello",
          affiliation = "Federal University of Santa Maria, Brazil and Federal University 
                         of Santa Maria, Brazil and Federal University of Santa Maria, 
                         Brazil and Federal University of Santa Maria, Brazil",
                title = "Low-Dose CT Dental Image Denoising by Morphological Operators and 
                         3D Filtering",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Low-dose, Computed tomography, Noise reduction, Mathematical 
                         Morphology, BM3D, PSNR.",
             abstract = "The impact in reducing the radiation dose in computed tomography 
                         (CT) exams is directly related to the quality of the images 
                         obtained in these exams. Such images are degraded by undesirable 
                         artifacts, known as noise. In order to improve the quality of 
                         these images and provide an accurate medical diagnosis, it is 
                         necessary to apply noise reduction techniques. In this study, a 
                         method based on structural segmentation and filtering through 
                         morphological operators along with a BM3D filtering is proposed to 
                         reduce noise and preserve details in low-dose CT dental images. 
                         Experimental results of the proposed method were compared with 
                         several existing methods and validated using the PSNR, SSIM, MSE 
                         and EPI metrics. Our method demonstrated superior performance 
                         among the evaluated filters. In comparison to the filter that 
                         obtained the best results, our method had a gain of 12.46% on 
                         PSNR, 11.11% on SSIM, 14.5% on MSE and 9.63% on EPI metrics.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00016",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00016",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U58J2S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U58J2S",
           targetfile = "Paper ID 12.pdf",
        urlaccessdate = "2024, Apr. 28"
}