Biomedical imaging facilities today like MRI, PET, CT scan and confocal microscopy produce sequentially parallel cross sectional images. Recon-structing trustworthy 3D explicit models which enable ...better understanding of the topology and shape of structure is crucial in facilitating diagnosis, improves surgical planning and aid in biological research.
Our technique produce a surface from G1 cross sectional contour curves of images. Surface accuracy is controlled by a tolerance measure. Features can be isolated and identified for corresspondence.
First the boundaries of the region of interest are extracted and corner points detected. G1 rational Bezier cubics, iteratively determined, are fitted piecewise between these corners and approximating the boundary. as close as need be. Adjacent contour curves are blended together to form the surface. Technique is fully automatic.
This paper proposes a scheme that speeds up the convergence of Markov Chain Monte Carlo (MCMC) algorithms in the context of limited-dependent variable models. The algorithm reduces autocorrelations ...more than the recently proposed Parameter Expansion Data Augumentation (PX-DA) algorithm. In addition, the paper provides an algorithm to sample a variance-covariance matrix with restrictions directly from the conditional posterior distribution. Finally, it is shown that the PX-DA algorithm, as applied to the multivariate probit model, can be seen as sampling from a different parameterization of the model. However, in some cases the PX-DA algorithm is not invariant to reparameterizations, and a slightly different algorithm is proposed.
The estimated genetic (Ĝ) and phenotypic (Pcirc) covariance matrices were reparametrized by canonical transformation such that Ĝ* became diagonal and Pcirc* an identity matrix. The reparametrization ...showed that G matrix was non-positive definite and needed to be modified before the construction of selection indices. The so-called "bending" method was used to modify the estimated parameters.
Consider the problem of inference about a parameter θ in the presence of a nuisance parameter v. In a Bayesian framework, a number of posterior distributions may be of interest, including the joint ...posterior of (θ, ν), the marginal posterior of θ, and the posterior of θ conditional on different values of ν. The interpretation of these various posteriors is greatly simplified if a transformation (θ, h(θ, ν)) can be found so that θ and h(θ, v) are approximately independent. In this article, we consider a graphical method for finding this independence transformation, motivated by techniques from exploratory data analysis. Some simple examples of the use of this method are given and some of the implications of this approximate independence in a Bayesian analysis are discussed.
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Some basic results by Browne (1974) on generalized least squares estimation in the analysis of covariance structures are extended to covariance structures with parameters subject to arbitrary ...nonlinear constraints. It is shown that the constrained estimators are consistent, asymptotically multivariate normally distributed, and asymptotically equivalent to constrained maximum likelihood estimators. Asymptotic chi-square tests are developed to evaluate appropriate model comparisons. The relationships between the Lagrangian approach and the reparameterization approach are discussed.
Some basic results by Browne (1974) on generalized least squares estimation in the analysis of covariance structures are extended to covariance structures with parameters subject to arbitrary ...nonlinear constraints. It is shown that the constrained estimators are consistent, asymptotically multivariate normally distributed, and asymptotically equivalent to constrained maximum likelihood estimators. Asymptotic chi-square tests are developed to evaluate appropriate model comparisons. The relationships between the Lagrangian approach and the reparameterization approach are discussed.
In a previous paper, it was shown that parameter-effects nonlinearities of a nonlinear regression model-experimental design-parameterization combination can be quantified by means of a ...parameter-effects curvature array A based on second derivatives of the model function. In this paper, the individual terms of A are interpreted and local compensation methods are suggested. A method of computing the parameter-effects array corresponding to a transformed set of parameters is given and we discuss how this result could be used to determine reparameterizations which have zero local parameter-effects nonlinearity.
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