Difference between revisions of "Algorithm:GATech"
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| − | = | + | = Current Projects = |
| − | == | + | == Segmentation == |
| − | === | + | === [[Algorithm:GATech:Multiscale_Shape_Segmentation|Multiscale Shape Segmentation Techniques]] === |
| − | + | To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. | |
| − | = | + | <font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum. |
| − | + | === [[Algorithm:GATech:Rule_Based_Segmentation|Rule-Based Segmentation Techniques]] === | |
| − | + | In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules. | |
| − | + | <font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007. | |
| − | === | + | === [[Algorithm:GATech:KPCA_Segmentation|Kernel PCA for Segmentation]] === |
| − | + | Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. | |
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| − | = | + | <font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI. |
| − | === | + | === [[Algorithm:GATech:Blood_Vessel_Segmentation|Blood Vessel Segmentation]] === |
| − | + | The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. | |
| − | = | + | <font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006 |
| − | + | === [[Algorithm:GATech:Knowledge_Based_Bayesian_Segmentation|Knowledge-Based Bayesian Segmentation]] === | |
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. | This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. | ||
| − | = | + | <font color="red">'''New: '''</font> J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006. |
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| − | === | + | === [[Algorithm:GATech:Stochastic_Methods_Segmentation|Stochastic Methods for Segmentation]] === |
| − | + | New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. | |
| − | = | + | <font color="red">'''New: '''</font> Currently under investigation. |
| − | + | == Registration == | |
| − | == | + | === [[Algorithm:GATech:Conformal_Flattening_Registration|Conformal Flattening]] === |
| − | + | The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. | |
| − | + | <font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006. | |
| − | === | + | === [[Algorithm:GATech:Optimal_Mass_Transport_Registration|Optimal Mass Transport Registration]] === |
| − | + | The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. | |
| − | = | + | <font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. Accepted for SPIE Conference on Computational Imaging, Jan 2007. |
| − | + | == DWI Processing == | |
| − | === | + | === [[Algorithm:GATech:Finsler_Active_Contour_DWI|Finsler Active Contour DWI Tractography]] === |
| − | + | In this work, we provide an energy minimization framework which allows one to find optimal curves in direction-dependent data (i.e. where the cost associated with the curve depends both upon its position and orientation) | |
| − | = | + | <font color="red">'''New: '''</font> E. Pichon, J. Melonakos, S. Angenet, A. Tannenbaum. Publication currently under review. |
| − | == | + | == Shape Analysis == |
| − | + | === [[Algorithm:GATech:Multiscale_Shape_Analysis|Multiscale Shape Analysis]] === | |
| − | + | Introductory sentence. | |
| − | [[NA-MIC/Projects/Structural/Shape_Analysis/3D_Shape_Analysis_Using_Spherical_Wavelets|3D Shape Analysis Using Spherical Wavelets]] | + | <font color="red">'''New: '''</font> Put the newest bullet here. [[NA-MIC/Projects/Structural/Shape_Analysis/3D_Shape_Analysis_Using_Spherical_Wavelets|Active Link: 3D Shape Analysis Using Spherical Wavelets]] |
| − | === | + | === [[Algorithm:GATech:Centerline_Generation_for_Vessels|Centerline Generation for Vessels]] === |
| − | + | The goal of this work is to generate centerlines from segmented 3D surfaces of blood vessels using a harmonic skeletonization technique. The generated centerlines are used as a guide to visualize and evaluate stenoses in human coronary arteries. | |
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| − | === | + | === [[Algorithm:GATech:KPCA_LLE_KLLE_Shape_Analysis|KPCA, LLE, KLLE Shape Analysis]] === |
| − | shape | + | The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. |
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= Completed Projects = | = Completed Projects = | ||
| − | == | + | == Segmentation == |
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| − | + | === [[Algorithm:GATech:Fast_Marching_Slicer_2|Fast Marching Slicer 2 Module]] === | |
| − | + | The Fast Marching Algorithm was added as a module to Slicer 2. | |
| − | + | === [[Algorithm:GATech:Statistical_Segmentation_Slicer_2|Statistical/PDE Methods for Segmentation]] === | |
| − | + | This flow was added to Slicer 2. | |
| − | + | === [[Algorithm:GATech:Image_Smooth_Slicer_2|Image Smooth Slicer 2 Module]] === | |
| − | + | 2D and 3D smoothing of images. | |
| − | + | === [[Algorithm:Affine_Segment_Slicer_2|Affine Segment Slicer 2 Module]] === | |
| − | + | This module can be used to perform edge based segmentation. | |
Revision as of 13:37, 18 December 2006
Home < Algorithm:GATechContents
Current Projects
Segmentation
Multiscale Shape Segmentation Techniques
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus.
New: Delphine Nain won the best student paper at MICCAI 2006 in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.
Rule-Based Segmentation Techniques
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules.
New: Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.
Kernel PCA for Segmentation
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors.
New: S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.
Blood Vessel Segmentation
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results.
New: Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006
Knowledge-Based Bayesian Segmentation
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter.
New: J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006.
Stochastic Methods for Segmentation
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation.
New: Currently under investigation.
Registration
Conformal Flattening
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner.
New: Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.
Optimal Mass Transport Registration
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence.
New: Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. Accepted for SPIE Conference on Computational Imaging, Jan 2007.
DWI Processing
Finsler Active Contour DWI Tractography
In this work, we provide an energy minimization framework which allows one to find optimal curves in direction-dependent data (i.e. where the cost associated with the curve depends both upon its position and orientation)
New: E. Pichon, J. Melonakos, S. Angenet, A. Tannenbaum. Publication currently under review.
Shape Analysis
Multiscale Shape Analysis
Introductory sentence.
New: Put the newest bullet here. Active Link: 3D Shape Analysis Using Spherical Wavelets
Centerline Generation for Vessels
The goal of this work is to generate centerlines from segmented 3D surfaces of blood vessels using a harmonic skeletonization technique. The generated centerlines are used as a guide to visualize and evaluate stenoses in human coronary arteries.
KPCA, LLE, KLLE Shape Analysis
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE.
Completed Projects
Segmentation
Fast Marching Slicer 2 Module
The Fast Marching Algorithm was added as a module to Slicer 2.
Statistical/PDE Methods for Segmentation
This flow was added to Slicer 2.
Image Smooth Slicer 2 Module
2D and 3D smoothing of images.
Affine Segment Slicer 2 Module
This module can be used to perform edge based segmentation.
