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12 May 2004 Numerical simulations of spectral phase and spectral distance behavior as scalar descriptors of multispectral data
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Multispectral imaging is the acquisition of images of the same view with multiple contrast mechanisms. Each voxel is a vector, each element of the vector being the value at that location for a particular spectral channel. We refer to this vector space as the feature space. Scalar measures computed from the feature space have been shown to improve visualization of objects. A wide variety of choices for the scalar measures are available. We used simulations to examine two simple scalar measures: spectral distance (SD) and spectral phase (SP). We assumed that we had two tissue compartments: background and lesion. The tissue characteristics in each compartment were described by Gaussian distributions with different means and variance of one. We used the mean background value as the reference point in the feature space. We compared SD and SP in terms of dependence on absolute locations of the spectral channels, correlation between spectral channels, and the number of spectral channels used (dimension). Each model was characterized by the L2 and L-infinity norms of the input signal-difference-to-noise ratio (SDNR) and the resulting SDNR. SP produced peak SDNR values which exceeded SD values. However, SP had a lower average SDNR value across the examined parameters. SP demonstrated a complex dependence on the correlation coefficient. For low SDNR in the input channels, formation of SD and SP images decreased the SDNR. The minimum additional SDNR depended on both the dimensionality of the model and the SDNR of the primary channel.
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Brian E. Chapman and Claudia Mello-Thoms "Numerical simulations of spectral phase and spectral distance behavior as scalar descriptors of multispectral data", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004);

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