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Abstract__Over the past decade, active shape models have gained increased popularity in medical image analysis. However, despite its widespread, it is now widely accepted that classical shape models using principle component analysis (PCA) is not able to faithfully model the wide range of variations that anatomical structures can undergo. In this paper, we present a new statistical shape model using wavelet transform and independent component analysis (ICA). In an attempt to benefit from the sparsification and approximation power of wavelets, we investigate constructing an ICA-based shape model in a compressed wavelet domain. In order to asses the efficiency of the proposed shape model; experiments were conducted using contours of human vertebrae from x-ray images

Statistical models of deformations are becoming crucial tools for a variety of computer vision applications such as regularization and validation of image registration and segmentation algorithms. In this article, we propose a new approach to effectively represent the statistical properties of high dimensional deformations. In particular, we propose techniques that use independent component analysis (ICA) in conjunction with wavelet packet decomposition. Two different architectures for ICA have been investigated; one treats the elastic deformations as random variables and the individual deformation field as outcomes and a second which treats the individual deformations as random variables and the elastic deformations as outcomes. The experiments were conducted using the Amsterdam Library of Images (ALOI), and the proposed algorithms were evaluated using the model generalization as a statistical measure. Experimental results show a significant improvement when compared to a recent deformation representation in the literature.

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