Showing papers in "Medical Image Analysis in 2006"

TL;DR: It is demonstrated that the standard active appearance model scheme performs poorly, but large improvements can be obtained by including areas outside the objects into the model, and a parameter optimization for active shape models is presented.

TL;DR: In this article, a deformable registration algorithm for diffusion tensor MR images is presented that enables explicit optimization of tensor reorientation. But the objective function captures both the image similarity and the smoothness of the transformation across region boundaries.

TL;DR: An overview of existing methods to correct intensity non-uniformity is proposed and the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view are presented.

TL;DR: This initiative of linking Schwann's 1838 cell theory with Schwarz's 1865 discovery of TPMS is a significant step to fabricate the previously elusive optimal biomorphic tissue analogs.

TL;DR: In this paper, a smooth vessel filter based on a geometrical analysis of the Hessian's eigensystem is proposed to enhance vascular structures within the framework of scale space theory.

TL;DR: In this paper, a smooth vessel filter based on a geometrical analysis of the Hessian's eigensystem with a non-linear anisotropic diffusion scheme is proposed to enhance vascular structures within the framework of scale space theory.

TL;DR: In this article, a 3D-ASM-based segmentation method was proposed for cardiac MRI image data sets consisting of multiple planes with arbitrary orientations, and with large undersampled regions.

TL;DR: This work proposes a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space and illustrates the potential of this new method to assess white matter fiber maturation and integrity.

TL;DR: A general-purpose registration algorithm for medical images and volumes built upon a differential multiscale framework and incorporates the expectation maximization algorithm, showing that this approach is highly effective in registering a range of synthetic and clinical medical images.

TL;DR: The approach is based on a hierarchical framework that is able to recover a globally consistent alignment of the input frames, to compensate for the motion distortions and to capture the non-rigid deformations of the imaged tissue.

TL;DR: A new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets is developed.

TL;DR: A mean geometric model for the end-diastolic heart has been built based on 27 cardiac CT datasets and has been evaluated with respect to its capability to estimate the position of cardiac structures.

TL;DR: It turns out that the selection step does have an added value for the system, while segmentation does not lead to a clear improvement.

TL;DR: A framework to estimate local ventricular myocardium contractility using clinical MRI, a heart model and data assimilation, and results on fitting to patient-specific anatomy and assimilation with simulated data are very promising.

TL;DR: A novel Lagrangian reference frame diffeomorphic image and landmark registration method that allows detailed study of the volumetric change between chimp and human cortex and applies to estimating a putative evolutionary change of coordinates between a population of chimpanzee and human cortices.

TL;DR: This work proposes and evaluates an intensity based automatic registration method using multiple portal images and the pre-treatment CT volume, and performs both geometric and radiometric calibrations to generate high quality digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery.

TL;DR: A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis, based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution.

TL;DR: The level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification and achieves comparable accuracy and reproducibility as the observers.

TL;DR: A Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases is presented.

TL;DR: The essential theoretical aspects underpinning adaptive, nonparametric Markov modeling and the theory behind the consistency of such a model are described.

TL;DR: An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas indicates significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

TL;DR: A comparison of in vivo and ex vivo data from the same organ has shown that the ex vivo mechanical response of the uterine cervix tissue does not differ considerably from that observed in vivo, and some differences can be identified in tissue pre-conditioning.

TL;DR: The proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF datasets acquired from two different MRI scanners, showing that it provides good quality of segmentation.

TL;DR: The findings of this study confirm the hypothesis that relatively lower wall shear stress is associated with early plaque development, and are based on the validated methodology for the three-dimensional fusion of intravascular ultrasound (IVUS) and X-ray angiography.

TL;DR: It is shown that by employing clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.

TL;DR: This work presents two methods to detect the needle in 2D ultrasound that specifically address needle curvature, and demonstrates how a new coordinate transformation can transform detection of a curved needle to a linear fit.

TL;DR: This paper presents a segmentation scheme for accurately extracting vasculature from MRA images that models capillary action and derives a capillary active contour for segmentation of thin vessels.

TL;DR: An original non-rigid image registration approach, which tends to improve the registration by establishing a symmetric image interdependence by measuring the image similarity in both registration directions.

TL;DR: This manuscript tackles the reconstruction of 3-D volumes via mono-modal registration of series of 2-D biological images (histological sections, autoradiographs, cryosections, etc.), and proposes a registration approach closely derived from this model.

TL;DR: An heuristic technique is proposed to quantify the amount of coherency at each point in the B-scans, which leads to an adapted elevational decorrelation scheme which allows for the coherent scattering in scans of real tissue.