Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Data
This work addresses a domain-specific problem for researchers and engineers in materials science and industrial imaging by providing a benchmark for segmentation techniques, though it is incremental as it builds on existing methods for data creation and evaluation.
The authors tackled the lack of a standard reference dataset for comparing segmentation algorithms for short glass fibers in industrial CT scans by introducing annotated real and synthetic CT data with evaluation metrics, and they provided an open-source Hessian-based filter as a baseline for performance studies.
Comparing different algorithms for segmenting glass fibers in industrial computed tomography (CT) scans is difficult due to the absence of a standard reference dataset. In this work, we introduce a set of annotated scans of short-fiber reinforced polymers (SFRP) as well as synthetically created CT volume data together with the evaluation metrics. We suggest both the metrics and this data set as a reference for studying the performance of different algorithms. The real scans were acquired by a Nikon MCT225 X-ray CT system. The simulated scans were created by the use of an in-house computational model and third-party commercial software. For both types of data, corresponding ground truth annotations have been prepared, including hand annotations for the real scans and STL models for the synthetic scans. Additionally, a Hessian-based Frangi vesselness filter for fiber segmentation has been implemented and open-sourced to serve as a reference for comparisons.