14 February 2012 Design of spectral filtering for tissue classification
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Abstract
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 calcification characterization.
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Ajay Narayanan, Ajay Narayanan, Pratik Shah, Pratik Shah, Bipul Das, Bipul Das, } "Design of spectral filtering for tissue classification", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142R (14 February 2012); doi: 10.1117/12.911195; https://doi.org/10.1117/12.911195
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