Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule

Zhixian Qiu; Jin-Gang Jiang; Dianhao Wu; Jingchao Wang; Shan Zhou

doi:10.1088/1361-6560/ad953f

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule

Phys Med Biol. 2024 Nov 20. doi: 10.1088/1361-6560/ad953f. Online ahead of print.

Authors

Zhixian Qiu¹, Jin-Gang Jiang², Dianhao Wu¹, Jingchao Wang³, Shan Zhou³

Affiliations

¹ Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Hexing Road Street, No. 52 Xuefu Road, Harbin university of science and technology, Harbin, Heilongjiang, 150080, CHINA.
² School of mechanical and power engineering, Harbin University of Science and Technology, Hexing Road Street, No. 52 Xuefu Road, Harbin university of science and technology, Harbin, Heilongjiang Province, 150080, CHINA.
³ Orthodontics, Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Harbin, Heilongjiang, 150001, CHINA.

PMID: 39569921
DOI: 10.1088/1361-6560/ad953f

Abstract

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Keywords: CBCT; Divergence; Euclidean Clustering; Point Cloud Resampling; Subregional resampling.