2019-03 iCT 2019 Short talks Thu 13:50 Auditorium Tools for the Analysis of Datasets from X-Ray Computed Tomography based on Talbot-Lau Grating Interferometry B. Fröhler1 6, L. Da Cunha Melo2 2, J. Weissenböck1 11, J. Kastner1 120, T. Möller3 2, H. Hege4 , E. Gröller2 11, J. Sanctorum5 3, J. De Beenhouwer5 15, J. Sijbers5 18, C. Heinzl1 38 1Research Group Computed Tomography; Upper Austrian University of Applied Sciences (FH OÖ) 166, Wels, Austria 2VRVis Research Center; Vienna University of Technolog (TU) 57, Vienna, Austria 3Faculty of Computer Science; University of Vienna 4, Vienna, Austria 4Konrad-Zuse-Institut Berlin (ZIB) 19, Berlin, Germany 5University of Antwerpen 34, Antwerpen, Belgium Radiographic Testing (RT), Other Methods, image fusion, multimodal data, transfer function design, image segmentation uncertainty
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This work introduces methods for analyzing the three imaging modalities delivered by Talbot-Lau grating interferometry X-ray
computed tomography (TLGI-XCT). The first problem we address is providing a quick way to show a fusion of all three modalities.
For this purpose the tri-modal transfer function widget is introduced. The widget controls a mixing function that uses the
output of the transfer functions of all three modalities, allowing the user to create one customized fused image. A second problem
prevalent in processing TLGI-XCT data is a lack of tools for analyzing the segmentation process of such multimodal data. We
address this by providing methods for computing three types of uncertainty: From probabilistic segmentation algorithms, from
the voxel neighborhoods as well as from a collection of results. We furthermore introduce a linked views interface to explore
this data. The techniques are evaluated on a TLGI-XCT scan of a carbon-fiber reinforced dataset with impact damage. We show
that the transfer function widget accelerates and facilitates the exploration of this dataset, while the uncertainty analysis methods
give insights into how to tweak and improve segmentation algorithms for more suitable results.
| iCT 2019 Session: Short talks Thu 13:50 Auditorium | 2019-03 |
Parameter-Space Exploration for Computed Tomography Image Analysis Algorithms B. Fröhler1 6, C. Heinzl1 38, J. Kastner1 120, T. Möller2,3 2 1Research Group Computed Tomography; Upper Austrian University of Applied Sciences (FH OÖ) 166, Wels, Austria 2Visualization and Data Analysis Research Group; University of Vienna 4, Vienna, Austria segmentation, Computed Tomography, visualization, parameter space exploration, multimodal segmentation, image processing
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We present a tool which supports CT specialists in exploring the parameter and result space of volume segmentation pipelines. In a preprocessing step, the user samples the parameter space of a number of pipelines to be analysed. Our sampling tool supports arbitrary pipeliness that can be parametrized via the command line. The user can then explore the space of possible results of the sampled pipelines in an interactive visualization tool. This visualization tool highlights the variation resulting from different parametrizations and pipelines, and allows exploring the influence of each parameter. We evaluate our tool on the multimodal segmentation of a carbon-fiber-reinforced polymer (CFRP) specimen with impact damage, scanned with a Talbot-Lau Grating Interferometer Computed Tomography device.
| iCT 2017 Session: Poster | 2017-03 |