- 1 Current Projects
- 1.1 Segmentation
- 1.2 Registration
- 1.3 DWI Processing
- 1.4 Shape Analysis
- 2 Completed Projects
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.
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.
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.
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
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.
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation.
New: Currently under investigation.
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.
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.
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.
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation.
New: D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Submitted to ISBI 2007.
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.
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.
The Fast Marching Algorithm was added as a module to Slicer 2.
This flow was added to Slicer 2.
2D and 3D smoothing of images.
This module can be used to perform edge based segmentation.