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Multi-spectral computed tomography is an emerging technology for thenon-destructive identification of object materials and the study of theirphysical properties. Applications of this technology can be found in variousscientific and industrial contexts, such as luggage scanning at airports.Material distinction and its identification is challenging, even with spectralx-ray information, due to acquisition noise, tomographic reconstructionartefacts and scanning setup application constraints. We present MUSIC - andopen access multi-spectral CT dataset in 2D and 3D - to promote furtherresearch in the area of material identification. We demonstrate the value ofthis dataset on the image analysis challenge of object segmentation purelybased on the spectral response of its composing materials. In this context, wecompare the segmentation accuracy of fast adaptive mean shift (FAMS) andunconstrained graph cuts on both datasets. We further discuss the impact ofreconstruction artefacts and segmentation controls on the achievable results.Dataset, related software packages and further documentation are made availableto the imaging community in an open-access manner to promote furtherdata-driven research on the subject

Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation