Development of Image Processing Filters for Improving Visibility of Fine Dentoalveolar Structures in Dental Cone-Beam Computed Tomography Images

Cone-beam computed tomography (CBCT) imaging in dentistry requires post-reconstruction image processing to enhance diagnostic quality while minimizing radiation exposure. Visualization of fine dentomaxillofacial structures, particularly the inferior alveolar canal (IAC) and dental pulp canals, presents significant diagnostic challenges in low-dose CBCT imaging. This study investigates the application of Wiener and adaptive Wiener (LLMMSE) filters in the reconstruction domain to improve the visibility of these critical anatomical structures in low-dose dental CBCT images. Two CBCT examinations of a dry mandible were acquired using reference and low-dose protocols. The low-dose post-reconstruction data was processed using six different filters: geometric mean, LLMMSE with additive noise 15, LLMMSE with additive noise 5, moving average, Wiener, and local contrast filters. These computationally efficient filters offer practical advantages over existing complex and costly noise reduction schemes. Subjective evaluation by an experienced oral and maxillofacial radiologist demonstrated that the IAC was clearly identifiable in all low-dose datasets regardless of filter application. However, the highest visibility of narrow pulp canals was achieved with the Wiener and LLMMSE_5 filters. This proof-of-concept study demonstrates the potential of Wiener and LLMMSE_5 techniques for improving visibility of narrow dental pulp canals in low-dose CBCT images, which has important implications for endodontic diagnosis and treatment planning while supporting radiation dose reduction strategies.

@article{Almutiry2026,
  title     = {Development of Image Processing Filters for Improving Visibility of Fine Dentoalveolar Structures in Dental Cone-Beam Computed Tomography Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Muhannad Almutiry and Asma’a Al-Ekrish and Saleh Alshebeili},
  doi       = {10.14569/IJACSA.2026.0170161},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170161}
}