1 October 2007 Partial volume and distribution estimation from multispectral images using continuous representations
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J. of Electronic Imaging, 16(4), 043001 (2007). doi:10.1117/1.2731782
Abstract
When estimating partial volume effects in the presence of noise, using neighboring information improves the estimation. The optimal linear transformation (OLT) is an unbiased minimum variance estimator. However, it does not use neighboring information and thus is sensitive to noise. We employ polynomial and B-spline continuous representations of the data to mathematically incorporate the neighboring information into the OLT. To evaluate the method, we use synthetic and actual images generated by simulation and acquired from phantoms and the human brain. Standard deviations of new estimators are up to 60% less than that of the OLT when the signal-to-noise ratio (SNR) is 25. As the SNR decreases, the proposed method demonstrates more improvements. Overall, B-spline estimators provide larger estimations of the standard deviation compared to polynomials. However, B-spline estimators outperform polynomials, providing an arbitrary degree of continuity. B-spline estimators are up to 10 times faster than polynomials and about 10 times slower than the OLT.
Mohammad-Reza Siadat, Hamid Soltanian-Zadeh, "Partial volume and distribution estimation from multispectral images using continuous representations," Journal of Electronic Imaging 16(4), 043001 (1 October 2007). http://dx.doi.org/10.1117/1.2731782
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KEYWORDS
Signal to noise ratio

Magnetic resonance imaging

Brain

Optical spheres

Data modeling

3D modeling

Network on a chip

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