Robust spatiotemporal quadrature filter for multiphase stepping.

A robust algorithm for phase recovery from multi-phase-stepping images is presented. This algorithm is based on the minimization of an energy (cost) functional and is equivalent to the simultaneous application of a fixed temporal quadrature filter and a spatial adaptive quadrature filter to the phase-stepping pattern ensemble. The algorithm, believed to be new, is specially suited for those applications in which a large number of phase-stepping images may be obtained, e.

Regularization methods for processing fringe-pattern images.

A powerful technique for processing fringe-pattern images is based on Bayesian estimation theory with prior Markov random-field models. In this approach the solution of a processing problem is characterized as the minimizer of a cost function with terms that specify that the solution should be compatible with the available observations and terms that impose certain (prior) constraints on the solution. We show that, by the appropriate choice of these terms, one can use this approach in almost every processing step for accurate and robust interferogram demodulation and phase unwrapping.

Supplemental perioperative oxygen and the risk of surgical wound infection: a randomized controlled trial.

Context: Supplemental perioperative oxygen has been variously reported to halve or double the risk of surgical wound infection.

Objective: To test the hypothesis that supplemental oxygen reduces infection risk in patients following colorectal surgery.

Design, Setting, And Patients: A double-blind, randomized controlled trial of 300 patients aged 18 to 80 years who underwent elective colorectal surgery in 14 Spanish hospitals from March 1, 2003, to October 31, 2004.

An accurate and efficient bayesian method for automatic segmentation of brain MRI.

Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans.