Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Elastic Surface Nets: Generating Smooth Surfaces from Binary Segmented Data
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Isosurface Reconstruction with Topology Control
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Surface Extraction from Multi-Material Components for Metrology using Dual Energy CT
IEEE Transactions on Visualization and Computer Graphics
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This paper describes a robust method for creating surface models from volume datasets with distorted density values due to artefacts and noise. Application scenario for the presented work is variance comparison and dimensional measurement of homogeneous industrial components in industrial high resolution 3D computed tomography (3D-CT). We propose a pipeline which uses common 3D image processing filters for pre-processing and segmentation of 3D-CT datasets in order to create the surface model. In particular, a pre-filtering step reduces noise and artefacts without blurring edges in the dataset. A watershed filter is applied on the gradient information of the smoothed data to create a binary dataset. Finally the surface model is constructed, using constrained elastic-surface nets to generate a smooth but feature preserving mesh of a binary volume. The major contribution of this paper is the development of the specific processing pipeline for homogeneous industrial components to handle large resolution data of industrial CT scanners. The pipeline is crucial for the following visual inspection of deviations.