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Hierarchical-multivariate spectral gradient method is proposed for unconstrained optimization problems. It is a compound of multivariate spectral gradient method and two-point stepsize gradient method but more flexible than them. And the related algorithm is applied into nonrigid registration of medical image.
Image registration based on gradient and least square optimization technique is one of the most edge-cutting registration algorithms. Such method, especially useful for sub-pixel motion, searches for the best motion in an iterative way. This paper solves the same motion registration problem following this direction. And the well-known Gauss-Newton method (GNM) is employed here as the optimization...
Geometric analysis of normal and autistic human subjects reveal distinctions in deformations in the corpus callosum (CC) that may be used for image analysis-based studies of autism. Preliminary studies showed that the CC of autistic patients is quite distinct from normal controls. We use an implicit vector representation of CC to carry out the registration process which reduces the pose differences...
We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite- time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial...
We present a fast and accurate framework for registration of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial information in the form of 'gradient intensity'. We introduce gradient intensity as a measure of spatial strength of an image in a given direction and show that it can be used to determine the rotational misalignment independent of translation...
This paper introduces a new method for shape registration by matching vector distance functions. The vector distance function representation is more flexible than the conventional signed distance map since it enables us to better control the shapes registration process by using more general transformations. Based on this model, a variational frame work is proposed for the global and local registration...
Conventional mutual information (Ml)-based registration using pixel intensities is time-consuming and ignores spatial information, which can lead to misalignment. We propose a method to overcome these limitation by acquiring initial estimates of transformation parameters. We introduce the concept of 'gradient intensity' as a measure of spatial strength of an image in a given direction. We determine...
This paper introduces a new method based on k-nearest neighbors graphs (KNNG) for bringing into alignment multiple views of the same scene acquired at two different time points. This framework is applied to cardiac motion estimation from tagging MRI sequences. Features acquired in each view are collected in a high dimensional feature space and an efficient estimator of alpha-joint entropy (alphaJE)...
Functional to anatomical brain image registration is needed for accurate localization of brain activation maps. Due to the presence of nonlinear distortions, it is more effective to consider non-rigid transformations to achieve such a registration. In this paper, a non-rigid registration technique based on the B-spline free-form deformation model and mutual information similarity measure is introduced...
In this work we propose a novel rigid image registration approach to determine the position of high-resolution molecular structures in medium-resolution macromolecular complexes. Mutual information similarity measure is used as an alternative to the cross-correlation coefficient commonly applied in this context. The optimum of the objective function is sought by means of differential evolution algorithm...
This paper presents a complete method estimating the displacement field of bodies constrained by an articulated model such as the neck area. Indeed bony structures between different patient images, such as vertebras, may rigidly move while other tissues may deform. The method is divided into 3 steps. The method first registers the articulated rigid bodies together. Then it propagates the deformation...
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