3D Teeth and Gum Segmentation Through Feature Fusion for Dental Image Processing

Accurate segmentation of teeth and gums plays a pivotal role in dental imaging, influencing both diagnostic accuracy and the efficacy of subsequent treatment strategies. Previous research predominantly focused on single feature methodologies which limits their ability to capture the complex and nuanced structures present in dental images. This study proposes a feature fusion approach to enhance segmentation performance by integrating multiple features and leveraging their complementary strengths. We introduce a multi-feature model that integrates a diverse set of features, encompassing intrinsic properties such as curvature and density, alongside texture information from Spin images, and local shape descriptors from Signature of Histograms of Orientations (SHOT) and Fast Point Feature Histogram (FPFH). We have developed a machine learning model that adeptly captures the intricate geometrical nuances of the oral cavity. The fusion of these features enables a dual focus on both the broader shape and the finer details, ensuring a thorough representation of dental structures. Our experiments showed that the feature fusion approach significantly improves segmentation accuracy and robustness. This comprehensive evaluation, which encompasses feature ablation studies and rigorous cross-validation, validates the superior performance of the model compared to traditional single feature methodologies. The optimal feature combination of Curvature, Density, and FPFH descriptors achieved an accuracy of 94.19%, with precision and recall rates of 94% and 94%, respectively. These figures represent a significant improvement over models utilising single features, highlighting the effectiveness of our feature fusion approach. The research methodology not only advances dental imaging but also fuels innovation in teeth aligner production. Applications include enhanced customisation, optimised treatment planning, improved aligner design, reduced manufacturing errors, streamlined digital workflow integration, and ongoing innovation in aligner technology.