Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models

Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery

Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources

Recurrent Canonical Piecewise Linear Network For Blind Equalization

The recurrent canonical piecewise linear (RCPL) network is applied to nonlinear blind equalization by generalizing Donoho's minimum entropy deconvolution approach. We first study the approximation ability of the canonical piecewise linear (CPL) network and the CPL based distribution learning for blind equalization. We then generalize these conclusions to the RCPL network. We show that nonlinear blind equalization can be achieved by matching the distribution of the channel input with that of the RCPL equalizer output. A new blind equalizer structure is constructed by using RCPL network and decision feedback. We discuss application of various cost functions to RCPL based equalization and present experimental results that demonstrate the successful application of RCPL network to blind equalization. 1. INTRODUCTION Blind equalization refers to the problem of determining the transmitted symbol sequence in the presence of intersymbol interference (ISI) and noise without using a training s..