Comparative in vitro study of contact- and image-based rigid registration for computer-aided surgery

A Bayesian theoretic approach to multiscale complex-phase-order representations

This paper explores a Bayesian theoretic approach to constructing multiscale complex-phase-order representations. We formulate the construction of complex-phase-order representations at different structural scales based on the scale-space theory. Linear and nonlinear deterministic approaches are explored, and a Bayesian theoretic approach is introduced for constructing representations in such a way that strong structure localization and noise resilience are achieved. Experiments illustrate its potential for constructing robust multiscale complex-phase-order representations with well-localized structures across all scales under high-noise situations. Illustrative examples of applications of the proposed approach is presented in the form of multimodal image registration and feature extraction.

An adaptive Monte Carlo approach to phase-based multimodal image registration

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.