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Partial volume and distribution estimation from multispectral images using continuous representations

[+] Author Affiliations
Mohammad-Reza Siadat

Oakland University, Department of Computer Science and Engineering, Rochester, Michigan 48309 and Henry Ford Health System, Department of Diagnostic Radiology, Radiology Research Image Analysis Laboratory, Detroit, Michigan 48202

Hamid Soltanian-Zadeh

Henry Ford Health System, Department of Diagnostic Radiology, Radiology Research Image Analysis Laboratory, Detroit, Michigan 48202 and University of Tehran, Department of the Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, Tehran 14395-515, Iran

J. Electron. Imaging. 16(4), 043001 (October 10, 2007). doi:10.1117/1.2731782
History: Received June 08, 2006; Revised November 16, 2006; Accepted January 09, 2007; Published October 10, 2007
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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.

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© 2007 SPIE and IS&T

Citation

Mohammad-Reza Siadat and Hamid Soltanian-Zadeh
"Partial volume and distribution estimation from multispectral images using continuous representations", J. Electron. Imaging. 16(4), 043001 (October 10, 2007). ; http://dx.doi.org/10.1117/1.2731782


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