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Variational Segmentation and PCA Applied to Dynamic PET Analysis
Parker, B. and Feng, D.D.
A graph-theoretic variational segmentation algorithm is applied to 22-frame dynamic positron emission tomography (PET) data sets after dimension reduction along the time axis using principal component analysis. Initial results indicate that the PCA is a very useful initial preprocessing step for segmentation and is effective in minimising the artifacts present in the PET data sets, allowing accurate delineation of pathological and anatomical features by the variational segmentation algorithm.
Cite as: Parker, B. and Feng, D.D. (2003). Variational Segmentation and PCA Applied to Dynamic PET Analysis. In Proc. Pan-Sydney Area Workshop on Visual Information Processing (VIP2002), Sydney, Australia. CRPIT, 22. Jin, J. S., Eades, P., Feng, D. D. and Yan, H., Eds. ACS. 89.
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