Adaptive Bayesian Shrinkage Model for Spherical Wavelet Based Denoising and Compression of Hippocampus Shapes

This paper presents a novel wavelet-based denoising and compression statistical model for 3D hippocampus shapes. Shapes are encoded using spherical wavelets and the objective is to remove noisy coefficients while keeping significant shape information. Todo so, we develop a non-linear wavelet shrinkage model based on a data-driven Bayesian framework. We threshold wavelet coefficients by locally taking into account shape curvature and interscale dependencies between neigh- boring wavelet coefficients. Our validation shows how this new wavelet shrinkage framework outperforms classical compression and denoising methods for shape representation. We apply our method to the denois- ing of the left hippocampus from MRI brain data.