Showing papers in "Medical Image Analysis in 2005"

TL;DR: The goal of MITK is to significantly reduce the effort required to construct specifically tailored, interactive applications for medical image analysis, by allowing an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features outside the scope of both.

TL;DR: An automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI) that is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.

TL;DR: The proposed image normalization technique will definitely improve automatic fundus images analysis but will also be very useful to eye specialists in their visual examination of retinal images.

TL;DR: A skeletonization algorithm which exploits properties of the average outward flux of the gradient vector field of a Euclidean distance function from the boundary of the structure is utilized to yield a collection of 3D curves, each of which is locally centered.

TL;DR: In this paper, deformable surfaces are represented as simplex meshes owing to their generality and their ability to compute mean curvature at each vertex in order to extend the deformable surface framework by introducing time-dependent constraints.

TL;DR: It could be shown that the approach presented is suitable for automated diagnosis and screening of glaucoma and is validated by comparing the performance of different classifiers on data from a case-control study with contours of the optic nerve head manually outlined by an experienced ophthalmologist.

TL;DR: It is demonstrated that this method performs comparably to an intensity method even when the images are related by a simple intensity transform, but that the phase method is significantly more robust to image artifacts which corrupt the ideal intensity mapping.

TL;DR: This work introduces an extension of the linear elastic tensor-mass method allowing fast computation of non-linear and visco-elastic mechanical forces and deformations for the simulation of biological soft tissue and develops a simulation tool for the planning of cryogenic surgical treatment of liver cancer.

TL;DR: A significant improvement in the alignment of the image sequences is achieved by the use of the deformable spatio-temporal transformation model, and the method is used for the construction of a probabilistic MR cardiac atlas representing the anatomy and function of a healthy heart.

TL;DR: Some of the progress that has been made in developing tailored methods for tagged cardiac MRI, as well as some of the ways these methods have been applied to the study of cardiac function are reviewed.

TL;DR: A novel kind of geometrical transformations, named polyrigid and polyaffine, efficiently code for locally rigid or affine deformations with a small number of intuitive parameters, which are smooth with respect to their parameters.

TL;DR: A scale-invariant registration method for 3D structures reconstructed from a monocular endoscopic camera to pre-operative CT-scans based on a previously presented method for reconstruction of a scaled 3D model of the environment from unknown camera motion is presented.

TL;DR: This work presents a system for statistical shape analysis using distance transforms for shape representation and the support vector machines learning algorithm for the optimal classifier estimation and demonstrates it on artificially generated data sets, as well as real medical studies.

TL;DR: A novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) ultrasound is proposed, which is a probabilistic Bayesian method and includes a volumetric extension of the prior that forces smoothness along the depth coordinate.

TL;DR: Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions.

TL;DR: In vitro experiments on bovine and porcine liver are presented in order to demonstrate the sensitivity of the proposed technique, and the reliability of the measurements is confirmed with comparative tests on synthetic material.

TL;DR: This work presents an approach toward the quantification of pulmonary deformation via non-rigid registration of serial MR images of the lung using the variational framework implemented in the Insight toolkit, and lays the groundwork for biomechanical modeling of the lungs using the finite element method.

TL;DR: A 3D geometric flow designed to segment the main core of fiber tracts in diffusion tensor magnetic resonance images, using a level set implementation to assure a stable and accurate evolution of the surface and to handle changes of topology of thesurface during the evolution process.

TL;DR: This report concludes that statistical methods employing summary measures for each attribute within each region of interest for each patient are preferable to methods that deal with individual vessels, that the distributions of the summary measures are indeed Gaussian, and that attribute values may differ by anatomical location.

TL;DR: A white matter tractography method based on an anisotropic wavefront propagation in diffusion tensor images and a validity index is described to rate the goodness of the resulting pathways with respect to the directionality of the tensor field.

TL;DR: Algorithms to model the motion of key anatomical structures and system implementations that enable the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making are developed.

TL;DR: The watershed segmentation technique showed improved subject interaction times and increased inter-subject precision over hand contouring, with quality that is visually and statistically comparable.

TL;DR: This learning-based method holds great promise for the automation of cardiac perfusion investigations, due to its accuracy, robustness and generalisation ability.

TL;DR: An analysis procedure is presented that enables the acquisition and visualization of physiologically relevant parameters using dynamic contrast-enhanced magnetic resonance imaging and the apparent improved representation of tissue vascularity when compared to conventional methods and the implications for this in treatment assessment are discussed.

TL;DR: The integration of anatomy, electrophysiology, and motion from patient data is presented and once fully validated, these models will make it possible to simulate different interventional strategies.

TL;DR: This work develops a two-step method comprising a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and a measurement step, yielding local and segmental deformation parameters over the whole myocardium.

TL;DR: A novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models is proposed and evaluated by comparing experimental results with expert manual segmentations to show the methodology achieves high quality segmentation results with computational efficiency.

TL;DR: A robust and fully automatic calibration method based on the Hough transform and robust estimators is proposed for calibration of three-dimensional (3D) freehand ultrasound called Confhusius (CalibratiON for FreeHand UltraSound Imaging USage).

TL;DR: The self-organizing map (SOM) is applied to a set of time curve feature vectors of single voxels from seven benign lesions and seven malignant tumors and shows clear potential for deriving visualization parameters in DCE MRI analysis.

TL;DR: An inverse method is presented to estimate the unknown boundary and volumetric forces necessary to achieve a least-squares fit between the model and the data that is formulated in terms of the adjoint equations, which are solved directly by the method of representers.