Local Quality Assessment for Surface Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for determining the surface of objects from volume data in tomography, such as CT scans, are limited by artifacts and low spatial resolution, leading to inaccurate surface detection and recognition, especially in industrial applications where precision is critical.
Innovation Solution
A method to assess the local quality of surface data extracted from volume data sets by evaluating criteria such as gray value profile sharpness, contrast, noise, and symmetry, which are used to assign quality parameters to each surface point, allowing for improved precision and reliability in surface determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surface determination methods are used on volume data with artifacts and limited spatial resolution, then the processing speed is maintained, but the measurement precision and reliability of surface detection deteriorate
Solution Approach 1:
The patent segments the surface determination process into multiple quality assessment criteria evaluated independently for each surface point. Each criterion (gray value profile sharpness, contrast, noise, symmetry) is calculated separately, allowing comprehensive quality evaluation without requiring a complete redesign of the surface determination system.
Solution Approach 2:
The patent performs preliminary quality assessment of surface points before final surface extraction. By evaluating multiple criteria and assigning quality parameters in advance, the system identifies unreliable surface points early, allowing users to make informed decisions about data validation and further processing.
2Reliability
If multiple quality criteria are evaluated for each surface point, then the reliability of surface determination improves, but the computational time and processing complexity increase
Solution Approach 1:
The patent evaluates multiple quality criteria beyond what a single conventional method provides, but does so in a modular fashion. Each criterion (sharpness, contrast, noise, symmetry) can be computed independently, allowing flexible adjustment of assessment depth based on application requirements.
Solution Approach 2:
The system uses the existing volume data and surface points to self-assess quality without requiring external reference data or complex additional measurements. The quality parameters are derived directly from the available gray value information and surface geometry.
3Manufacturing precision
If surface points with low quality parameters are excluded from further processing, then the manufacturing precision improves, but the quantity of usable surface data decreases
Solution Approach 1:
The patent applies local quality assessment to individual surface points rather than treating the entire surface uniformly. Each point receives a quality parameter based on local gray value characteristics, allowing selective exclusion of only those points affected by artifacts while preserving the majority of valid surface data.
Solution Approach 2:
The system changes the state of surface data by adding quality parameters to each surface point. This additional parameter information enables intelligent filtering and selection, transforming raw surface points into quality-annotated data that can be selectively processed based on application needs.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The aim of the invention is to determine the local quality of surface data (O) extracted from a volume data set (V) by means of a surface determination method. An environment in the volume data set (V) is determined for each surface point of the surface data (O). Using the curve of the grayscale values of voxels from said environment, at least one quality characteristic (Q) is derived which characterizes the quality of the respective examined surface point. The quality characteristic (Q) or each quality characteristic is output together with coordinates of the respective examined surface point as the method result (O').