  In this work, we have investigated a number of unsupervised learning methods
for material segmentation in projection x-ray imaging with a spectral detector.
A phantom containing two hard materials (glass, steel) and three soft materials
(PVC, polypropylene, and PFTE) all embedded in PMMA was imaged with a 5 energy
bin spectal detector. The projection images were utilized to test nine
unsupervised learning algorithms for automated material segmentation. Each
algorithm was investigated using single energy (SE), dual energy (DE) and multi
energy (ME) images. Clustering results were scored based on homogeneity and
completeness of the clusters, which were combined into the Rosenberg and
Hirshberg's V-measure. Principle component analysis (PCA), independent
component analysis (ICA), and non-negative matrix factorization (NMF) were
tested as dimensional reduction methods. ME, DE and SE material segmentation
was performed using five, two, and single energy images, respectively. ME had
the highest V-measure on the soft materials using PCA and a novel interpolating
bayesian gaussian mixture model (BGMM) clustering with a V-measure of 0.71.
This was by 3.5% better than DE and 20.3% better than SE. Conversely, SE
imaging was most capable of hard tissue segmentation using the standard BGMM,
with a V-measures of 0.84. This was 6.3% better than DE and 5.0% better than
ME. This work demonstrated that ME x-ray imaging might be superior in
segmenting soft tissues compared to conventional SE x-ray imaging.
