  Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular
structures at the submolecular resolution in close to the native state.
However, due to the high degree of structural complexity and imaging limits,
the automatic segmentation of cellular components from ECT images is very
difficult. To complement and speed up existing segmentation methods, it is
desirable to develop a generic cell component segmentation method that is 1)
not specific to particular types of cellular components, 2) able to segment
unknown cellular components, 3) fully unsupervised and does not rely on the
availability of training data. As an important step towards this goal, in this
paper, we propose a saliency detection method that computes the likelihood that
a subregion in a tomogram stands out from the background. Our method consists
of four steps: supervoxel over-segmentation, feature extraction, feature matrix
decomposition, and computation of saliency. The method produces a distribution
map that represents the regions' saliency in tomograms. Our experiments show
that our method can successfully label most salient regions detected by a human
observer, and able to filter out regions not containing cellular components.
Therefore, our method can remove the majority of the background region, and
significantly speed up the subsequent processing of segmentation and
recognition of cellular components captured by ECT.
