Statistical simulation of deformations using wavelet independent component analysis

Abstract

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.

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

“…Rose, and Taylor [16], proposed a statistical model for mammographic appearance using steerable decomposition and PCA modeling. In [17], ElSafi et al proposed a method for statistical simulation of deformations using wavelet Independent Component Analysis. In [18], Xue et al presented a method for simulating of deformations of MR brain images for validation of Atlas-based segmentation and registration algorithms.…”