  Multi-spectral computed tomography is an emerging technology for the
non-destructive identification of object materials and the study of their
physical properties. Applications of this technology can be found in various
scientific and industrial contexts, such as luggage scanning at airports.
Material distinction and its identification is challenging, even with spectral
x-ray information, due to acquisition noise, tomographic reconstruction
artefacts and scanning setup application constraints. We present MUSIC - and
open access multi-spectral CT dataset in 2D and 3D - to promote further
research in the area of material identification. We demonstrate the value of
this dataset on the image analysis challenge of object segmentation purely
based on the spectral response of its composing materials. In this context, we
compare the segmentation accuracy of fast adaptive mean shift (FAMS) and
unconstrained graph cuts on both datasets. We further discuss the impact of
reconstruction artefacts and segmentation controls on the achievable results.
Dataset, related software packages and further documentation are made available
to the imaging community in an open-access manner to promote further
data-driven research on the subject
