Showing papers in "Medical Image Analysis in 2000"

TL;DR: The 'snakes in ACID' framework significantly extends conventional snakes, enabling topological flexibility among other features and can be used to segment some of the most complex-shaped biological structures from medical images in an efficient and highly automated manner.

TL;DR: A novel image segmentation algorithm was developed, in which an explicit anatomical template is used to moderate the segmentation obtained by statistical classification, which forms an adaptive, template moderated, spatially varying statistical classification (SVC).

TL;DR: A literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject is presented and the state of the art among existing techniques is reviewed.

TL;DR: This paper proposes a phase-based feature detection method to be used as the front end to higher-level 2D+T/3D-T reconstruction algorithms and shows how this temporal-based algorithm helps to reduce the number of spurious feature responses due to speckle and provides feature velocity estimates.

TL;DR: This paper describes prototype Image Overlay systems and initial experimental results from those systems, and describes how the images are transformed in real-time so they appear to the user to be an integral part of the surrounding environment.

TL;DR: In this paper, the cortical folds can be subdivided into a number of substructures which are called sulcal basins, which are then segmented using a region growing approach.

TL;DR: The goal of the present work is to develop a framework for the full-scale, real-time, finite element simulation of elastic tissue deformation in complex systems such as the human abdomen.

TL;DR: 3D generalization of the live-wire approach for segmenting 3D/4D object boundaries which further reduces the time spent by the user in segmentation is introduced.

TL;DR: A method for reconstructing the 3D motion of the RV by fitting a deformable model to tag and contour data extracted from multiview tagged magnetic resonance images, which accommodates the geometrically complex RV.

TL;DR: Two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models are described, showing that statistical boundary shape information significantly augments and improves physical model-based non- Rigid registration.

TL;DR: A general method to study the dissymmetry of anatomical structures such as those found in the human brain, which relies on the estimate of 3D dis Symmetry fields, the use of3D vector field operators, and T2 statistics to compute significance maps.

TL;DR: A new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set is presented.

TL;DR: This paper proposes to model the spatio-temporal myocardial displacement field by a cosine series model fitted to the entire tagged dataset and provides an analytical and hierarchical model of the 2D+time deformation inside the myocardium.

TL;DR: A method for 3D segmentation from voxel data which combines statistical classification and geometry-driven segmentation is described and how the partial volume effect is estimated and object measurements are obtained is discussed.

TL;DR: This paper presents a new approach for segmenting regions of bone in MRI volumes using deformable models, which takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions.

TL;DR: A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented and clearly shows an improvement over conventional resampled techniques such as cubic spline interpolation and sinc interpolation.

TL;DR: Simulation of a set of post-operative X-ray images of 2-D/3-D registration for robot-assisted total hip replacement (THR) surgery and a new framework for incorporating surface normals in the objective function for registration are introduced.

TL;DR: The detection of symmetry axes through the optimization of a given symmetry measure, computed as a function of the mean-square error between the original and reflected images, is investigated and the application to skin cancer diagnosis is illustrated and discussed.

TL;DR: The examples here show how these representations extract statistically informative and scientifically helpful features from deformations that help characterize two brain diseases, schizophrenia and Fetal Alcohol Syndrome, in two dimensions.

TL;DR: A new technique for assessing the accuracy of segmentation algorithms, applied to the performance evaluation of brain editing and brain tissue segmentations algorithms for magnetic resonance images is presented, and a performance evaluation criteria derived from the use of the realistic digital brain phantom Brainweb is proposed.

TL;DR: This paper focuses on the task of automatic feature detection for intra-operative drilling trajectory planning for computer-assisted internal fixation of hip fractures, using summed 1D grey level profiles as a main segmentation tool to carry out the above strategy.

TL;DR: It is concluded that some of the fast, high resolution 3D gradient echo pulse sequences that have become available on conventional clinical scanners can be used to obtain good estimates of brain cerebrospinal fluid anatomical distribution and volume.

TL;DR: A method for quickly re-rendering volume data consisting of several distinct materials and intermixed with moving geometry is presented, which facilitates accelerated rendering of fixed views at moderate storage overhead without re-scanning the entire volume.

TL;DR: This work extends the Cutting et al. wireframe approach by encoding the entire surface as a series of B-spline space curves and provides an average that well represents the source images and may be useful clinically as a deformable model or for animation.

TL;DR: A generally valid scheme for determining the standard deviation of distance measurements in 3D to 2D projections has been worked out and applied to four previously published methods for measuring the migration of the prosthetic cup using standard radiographs.

TL;DR: A framework for the detection of the brain boundary (arachnoid) within sparse MRI is introduced using a constrained mesh surface which iteratively approximates a 3D point set consisting of detected boundary points.