Automated 3D Dental Data Alignment Using Spline Curve Search
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Solution Overview
Problem
The registration of 3D dental CT images and digital impression models is time-consuming and labor-intensive, and existing methods using landmark points may not accurately align data, especially when there are differences in data areas or missing teeth.
Innovation Solution
An automated method that extracts landmark points from both CT and digital impression model data, determines an up vector to identify left and right landmarks, extracts teeth portions, and searches for candidate target points on a spline curve to align the data, reducing the need for user input and improving alignment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If landmark points are manually selected for registration, then alignment accuracy can be achieved, but time and effort are significantly consumed
Solution Approach 1:
The system automatically extracts landmark points and performs registration without requiring manual user selection. The computer extracts landmark points from both the dental CT image and digital impression model, calculates transformation parameters automatically, and completes the registration process autonomously, eliminating the need for user intervention while maintaining accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of landmark point selection with an automated computational system. The system uses coordinate extraction algorithms and transformation calculations to automatically identify corresponding points and compute alignment parameters, substituting human manual operation with automated mathematical processing.
2Loss of time
If automated landmark point extraction is used, then registration time is reduced, but alignment accuracy deteriorates when data areas differ or teeth are missing
Solution Approach 1:
The system dynamically adjusts the registration approach based on data characteristics. It extracts multiple types of landmark points (anatomical landmarks, teeth landmarks, occlusion plane landmarks) and selects appropriate transformation methods based on the specific conditions of the dental CT and digital impression data, such as whether teeth are present or data areas match.
Solution Approach 2:
The patent introduces an occlusion plane as an intermediary reference element. By extracting landmarks from the occlusion plane in addition to traditional anatomical landmarks, the system creates additional correspondence points that help maintain accuracy even when teeth are missing or data areas differ between the two datasets.
3Measurement precision
If multiple landmark points are extracted for better accuracy, then the complexity of the registration process increases
Solution Approach 1:
The patent divides the landmark point extraction into distinct categories: anatomical landmarks (three or more points), teeth landmarks (when teeth are present), and occlusion plane landmarks. This segmentation allows the system to process different types of landmarks separately and combine their results, managing complexity through structured organization of the extraction process.
Solution Approach 2:
The system employs a universal transformation calculation framework that can handle multiple types of landmark points and different registration scenarios through a single integrated process. The same mathematical transformation methods are applied regardless of which types of landmarks are extracted, providing a unified approach that reduces overall process complexity.
Data Source
AI summary
An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.