Tissue characterization from imaging studies is an integral part of clinical practice. We describe a spectral filter
design for tissue separation in dual energy CT scans obtained from Gemstone Spectral Imaging scanner. It
enables to have better 2D/3D visualization and tissue characterization in normal and pathological conditions.
The major challenge to classify tissues in conventional computed tomography (CT) is the x-ray attenuation
proximity of multiple tissues at any given energy. The proposed method analyzes the monochromatic images
at different energy levels, which are derived from the two scans obtained at low and high KVp through fast
switching. Although materials have a distinct attenuation profile across different energies, tissue separation
is not trivial as tissues are a mixture of different materials with range of densities that vary across subjects.
To address this problem, we define spectral filtering, that generates probability maps for each tissue in multi-energy
space. The filter design incorporates variations in the tissue due to composition, density of individual
constituents and their mixing proportions. In addition, it also provides a framework to incorporate zero mean
Gaussian noise. We demonstrate the application of spectral filtering for bone-free vascular visualization and
In this paper, we present a framework that one could use to set optimized parameter values, while performing
image registration using mutual information as a metric to be maximized. Our experiment details these steps
for the registration of X-ray Computer Tomography (CT) images with Positron Emission Tomography (PET)
images. Selection of different parameters that influence the mutual information between two images is crucial
for both accuracy and speed of registration. These implementation issues need to be handled in an orderly
fashion by designing experiments in their operating ranges. The conclusions from this study seem vital towards
obtaining allowable parameter range for a fusion software.