The Experts below are selected from a list of 159660 Experts worldwide ranked by ideXlab platform
Nick C Fox - One of the best experts on this subject based on the ideXlab platform.
-
Issues with threshold masking in Voxel-based morphometry of atrophied brains
NeuroImage, 2009Co-Authors: Nick C FoxAbstract:There is great interest in using automatic computational neuroanatomy tools to study ageing and neurodegenerative disease. Voxel-Based Morphometry (VBM) is one of the most widely used of such techniques. VBM performs Voxel-wise statistical analysis of smoothed spatially normalised segmented Magnetic Resonance Images. There are several reasons why the analysis should include only Voxels within a certain mask. We show that one of the most commonly used strategies for defining this mask runs a major risk of excluding from the analysis precisely those Voxels where the subjects’ brains were most vulnerable to atrophy. We investigate the issues related to mask construction, and recommend the use of alternative strategies which greatly decrease this danger of false negatives.
Paul Suetens - One of the best experts on this subject based on the ideXlab platform.
-
An information theoretic approach for non-rigid image registration using Voxel class probabilities
Medical Image Analysis, 2006Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
-
WBIR - An Information Theoretic Approach for Non-rigid Image Registration Using Voxel Class Probabilities
Biomedical Image Registration, 2003Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
-
MICCAI (2) - An Information Theoretic Approach for Non-rigid Image Registration Using Voxel Class Probabilities
Lecture Notes in Computer Science, 2003Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
Ke Zhang - One of the best experts on this subject based on the ideXlab platform.
-
brain tumor classification of virtual nmr Voxels based on realistic blood vessel induced spin dephasing using support vector machines
NMR in Biomedicine, 2020Co-Authors: Artur Hahn, Julia Bode, Sarah Schuhegger, Thomas Kruwel, Volker Sturm, Ke ZhangAbstract:Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI Voxel. We have numerically simulated the expected transverse relaxation in NMR Voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR Voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual Voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI Voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single Voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated Voxel-by-Voxel signal interpretation to detect vascular pathologies.
Emiliano D'agostino - One of the best experts on this subject based on the ideXlab platform.
-
An information theoretic approach for non-rigid image registration using Voxel class probabilities
Medical Image Analysis, 2006Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
-
WBIR - An Information Theoretic Approach for Non-rigid Image Registration Using Voxel Class Probabilities
Biomedical Image Registration, 2003Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
-
MICCAI (2) - An Information Theoretic Approach for Non-rigid Image Registration Using Voxel Class Probabilities
Lecture Notes in Computer Science, 2003Co-Authors: Emiliano D'agostino, Frederik Maes, Dirk Vandermeulen, Paul SuetensAbstract:We propose a multimodal free-form registration algorithm that matches Voxel class labels rather than image intensities. Individual Voxels are displaced such as to minimize the Kullback-Leibler distance between the actual and ideal joint probability distribution of Voxel class labels, which are assigned to each image individually by a previous segmentation process. We evaluate the performance of the method for inter-subject brain registration with simulated deformations, using a viscous fluid model for regularization. The root mean square difference between recovered and ground truth deformations is smaller than 1 Voxel.
Arie E Kaufman - One of the best experts on this subject based on the ideXlab platform.
-
fast ray tracing of rectilinear volume data using distance transforms
IEEE Transactions on Visualization and Computer Graphics, 2000Co-Authors: M Sramek, Arie E KaufmanAbstract:The paper discusses and experimentally compares distance based acceleration algorithms for ray tracing of volumetric data with an emphasis on the Chessboard Distance (CD) Voxel traversal. The acceleration of this class of algorithms is achieved by skipping empty macro regions, which are defined for each background Voxel of the volume. Background Voxels are labeled in a preprocessing phase by a value, defining the macro region size, which is equal to the Voxel distance to the nearest foreground Voxel. The CD algorithm exploits the chessboard distance and defines the ray as a nonuniform sequence of samples positioned at Voxel faces. This feature assures that no foreground Voxels are missed during the scene traversal. Further, due to parallelepipedal shape of the macro region, it supports accelerated visualization of cubic, regular, and rectilinear grids. The CD algorithm is suitable for all modifications of the ray tracing/ray casting techniques being used in volume visualization and volume graphics. However, when used for rendering based on local surface interpolation, it also enables fast search of intersections between rays and the interpolated surface, further improving speed of the process.
-
a ray slice sweep volume rendering engine
International Conference on Computer Graphics and Interactive Techniques, 1997Co-Authors: Ingmar Bitter, Arie E KaufmanAbstract:Ray-slice-sweeping is a plane sweep algorithm for volume rendering, The compositing buffer sweeps through the volume and combines the accumulated image with the new slice of just-projected Voxels. The image combination is guided by sight rays from the view point through every Voxel of the new slice. Cube-.#L is a volume rendering architecture which employs a ray-slice-sweeping algorithm. It improves the Cube-4 architecture in three ways. First, during perspective projection all Voxels of the dataset contribute to the rendering. Second, it computes gradients at the Voxel positions which improves accuracy and allows a more compact implementation, Third, Cube-AL has less control overhead than Cube-C
