Low-Dose CT Dental Image Denoising by Morphological Operators and 3D Filtering

  • Romulo Marconato Stringhini Federal University of Santa Maria
  • Daniel Welfer Federal University of Santa Maria
  • Daniel Fernando Tello Gamarra Federal University of Santa Maria
  • Marcos Cordeiro d'Ornellas Federal University of Santa Maria


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.

Palavras-chave: Low dose, Computed Tomography, Noise reduction, Mathematical morphology, BM3D, PSNR


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STRINGHINI, Romulo Marconato; WELFER, Daniel; GAMARRA, Daniel Fernando Tello ; D'ORNELLAS, Marcos Cordeiro. Low-Dose CT Dental Image Denoising by Morphological Operators and 3D Filtering. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . DOI: https://doi.org/10.5753/sibgrapi.2019.9797.