Accuracies of a point-based and an intensity-based fluoroscopic methods of assessing patella tracking were determined by comparing the pattern of patellar motion with respect to orientation (flexion, internal rotation...
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Accuracies of a point-based and an intensity-based fluoroscopic methods of assessing patella tracking were determined by comparing the pattern of patellar motion with respect to orientation (flexion, internal rotation, and lateral tilt) and translation (lateral, proximal, and anterior) with the pattern of patellar motion measured using Roentgen stereophotogrammetric analysis in three cadaver knee specimens. Each pose in the patellar motion could be obtained from single as well as multiple calibrated fluoroscopic images. The errors using the intensity-based method were slightly higher than those of the point-based method, but they appear to be sufficiently low to detect clinically significant differences in patellar kinematics. (C) 2004 Elsevier B.V. All rights reserved.
We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the object shape and the image gray levels, along with information derived from the input image, is ...
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We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the object shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of an object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation model using the joint prior information of the object shape and the image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define a joint probability distribution over the variations of the object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-intensity model in terms of level set functions as opposed to landmark points of the object shape. In addition, we evaluate the performance of the level set representation of the object shape by comparing it with the point distribution model (PDM). We found the algorithm to be robust to noise and able to handle multidimensional data, while able to avoid the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D and 3D medicalimages are shown. (C) 2004 Elsevier B.V. All rights reserved.
Optimization of a similarity metric is an essential component in most medicalimage registration approaches based on image intensities. The increasing availability of parallel computers makes parallelizing some regist...
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We have developed a new approach for preoperative selection of points from a surface model for rigid shape-based registration. This approach is based on an extension of our earlier spatial-stiffness model of fiducial ...
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The proceedings contain 111 papers. The special focus in this conference is on Brain Segmentation, Cardiovascular Segmentation, Segmentation Methods and Registration. The topics include: Level set methods in an EM fra...
ISBN:
(纸本)3540229760
The proceedings contain 111 papers. The special focus in this conference is on Brain Segmentation, Cardiovascular Segmentation, Segmentation Methods and Registration. The topics include: Level set methods in an EM framework for shape classification and estimation;segmentation of 3D probability density fields for 4D ultrasound data;learning coupled prior shape and appearance models for segmentation;a modified total variation denoising method in the context of 3d ultrasound images;correcting nonuniformities in MRI intensities using entropy minimization based on an elastic model;texture image analysis for osteoporosis detection with morphological tools;dual front evolution model and its application in medical imaging;topology smoothing for segmentation and surface reconstruction;simultaneous boundary and partial volume estimation in medicalimages;medicalimage segmentation based on mutual information maximization;adaptive segmentation of multi-modal 3d data using robust level set techniques;coupling statistical segmentation and PCA shape modeling;image segmentation adapted for clinical settings by combining pattern classification and level sets;profile scale-spaces for multiscale image match;classification improvement by segmentation refinement;landmark-driven, atlas based segmentation of mouse brain tissue images containing gene expression data;on normalized convolution to measure curvature features for automatic polyp detection and robust generalized total least squares iterative closest point registration;quantification of delayed enhancement MR images and automatic optimization of segmentation algorithms through simultaneous truth and performance level estimation.
A method is presented to non-rigidly register lateral ventricles to enable the automated analysis of peri-ventricular white matter lesions. A binary average image of the lateral ventricle system is used as a reference...
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The evaluation of tissue perfusion in various parenchymatous organs is important in the diagnosis and determination of the severity of ischemic disease. Contrast ultrasound perfusion imaging can be used for this purpo...
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A method for processing images prior to normalized mutual information based registration and an enhancement of the registration measure are presented. The method relies on k-means clustering of the intensity distribut...
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In this paper, we propose local watershed operators for the segmentation of medical structures. Watershed transform is a powerful technique to partition an image into many regions while retaining edge information very...
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The use of statistical shape models in medicalimage analysis is growing due to the ability to incorporate prior organ shape knowledge for tasks such as segmentation, registration, and classification. Shape models are...
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