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PCA vs. Tensor-Based Dimension Reduction Methods: An Empirical Comparison on Active Shape Models of Organs
 

Summary: PCA vs. Tensor-Based Dimension Reduction Methods: An Empirical
Comparison on Active Shape Models of Organs
Jiun-Hung Chen and Linda G. Shapiro
Abstract-- How to model shape variations plays an important
role in active shape models that is widely used in model-
based medical image segmentation, and principal component
analysis is a common approach for this task. Recently, different
tensor-based dimension reduction methods have been proposed
and have achieved better performances than PCA in face
recognition. However, how they perform in modeling 3D shape
variations of organs in terms of reconstruction errors in medical
image analysis is still unclear.
In this paper, we propose to use tensor-based dimension
reduction methods to model shape variations. We empirically
compare two-dimensional principal component analysis, the
parallel factor model and the Tucker decomposition with PCA
in terms of the reconstruction errors. From our experimental
results on several different organs such as livers, spleens and
kidneys, 2DPCA performs best among the four compared
methods, and the performance differences between 2DPCA and

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences