Analysis of optical propagation in thick holographic gratings

A method of analyzing optical propagation in thick holographic gratings by decomposition of the thick material into thin gratings is discussed. The method is readily applicable to study propagation in multiple gratings of arbitrary spatial frequency and orientation recorded in the same thick emulsion. Applied to the double grating case, the method predicts strong cross-coupling between the two gratings for proper relative slope of the gratings. Results are given.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47036/1/339_2004_Article_BF00900516.pd

Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps

Abstract. In this paper we present improvements to our Bayesian approach fordescribing the position distribution of the endocardium in cardiac ultrasound imagesequences. The problem is represented as a latent variable model, which representsthe inside and outside of the endocardium, for which the posterior densityis estimated. We start our construction by assuming a three-component Rayleighmixture model: for blood, echocardiographic artifacts, and tissue. The Rayleighdistribution has been previously shown to be a suitable model for blood and tissuein cardiac ultrasound images. From the mixture model parameters we build a latentvariable model, with two realizations: tissue and endocardium. The model isrefined by incorporating priors for spatial and temporal smoothness, in the formof total variation, connectivity, preferred shapes and position, by using the principalcomponents and location distribution of manually segmented training shapes.The posterior density is sampled by a Gibbs method to estimate the expected latentvariable image which we call the Bayesian ProbabilityMap, since it describesthe probability of pixels being classified as either heart tissue or within the endocardium.By sampling the translation distribution of the latent variables, weimprove the convergence rate of the algorithm. Our experiments show promisingresults indicating the usefulness of the Bayesian Probability Maps for theclinician since, instead of producing a single segmenting curve, it highlights theuncertain areas and suggests possible segmentations