Metal Artifact Suppression in Dental Cone Beam Computed Tomography Images Using Image Processing Techniques

Area of Science:

• Dental Imaging
• Medical Image Processing
• Radiology

Background:

• Dental cone beam computed tomography (CBCT) images are often degraded by severe metal artifacts.
• Streaking artifacts and cavities around teeth significantly reduce image quality and clinical utility.

Purpose of the Study:

• To develop and present a novel artifact reduction algorithm for dental CBCT images.
• To improve the visual quality and diagnostic compatibility of dental CBCT scans.

Main Methods:

• A three-component parallel algorithm was developed for artifact reduction.
• Teeth extraction using Gaussian mixture model histogram analysis.
• Artifact reduction via polar domain conversion and morphological filtering.
• Cavity filling using morphological filtering.

Main Results:

• A fusion step combines the outputs of the three components.
• The combined approach yields visually improved images compatible with human perception.

Conclusions:

• The proposed algorithm effectively reduces artifacts in dental CBCT images.
• The method produces clean, high-quality images suitable for dental diagnostics.