PCA vs. tensor-based dimension reduction methods: An empirical comparison on active shape models of organs

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 the other methods are statistically significant.