Image segmentation with the combination of the PCA- and ICA-based modes of shape variation

Abstract

How to constrain the deformations in deformable model-based image segmentation is a well-studied issue. Many methods that use the modes of shape variation generated from a training set shapes have been introduced. Most of these methods rely on principle component analysis (PCA) to statistically model the variability in the training set. Independent component analysis (ICA) has been proposed for this purpose, too. In this paper, we combine the PCA- and ICA-based modes of shape variation using a consecutive approach: an a priori model is deformed first by the PCA modes, which represent the global shape variability in the training set, and then, by the ICA modes, which have a more local character. The method is validated using a set of three-dimensional (3D) brain MR images. The results prove that by applying the ICA modes after the PCA modes the accuracy of image segmentation is statistically significantly (p < 0.05) improved.