IEEE VIS 2024 Content: Bimodal Visualization of Industrial X-ray and Neutron Computed Tomography Data

Bimodal Visualization of Industrial X-ray and Neutron Computed Tomography Data

Huang, Xuan -

Miao, Haichao -

Kim, Hyojin -

Townsend, Andrew -

Champley, Kyle -

Tringe, Joseph -

Pascucci, Valerio -

Bremer, Peer-Timo -

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Room: Bayshore V

2024-10-17T18:09:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T18:09:00Z
Exemplar figure, described by caption below
The X-Ray and neutron computed tomography industrial object XR05, consisting of multiple materials and intrinsic structures. With a morse-complex based segmentation (bottom left) on the bivariate histogram combing two modalities (top left), we present an efficient yet flexible system for examining material compositions (right).
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Abstract

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of multimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive multimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a multimodal isualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large multimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization echniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress multimodal scanning techniques, and real-world objects, and we discuss expert feedback.