MICCAI 2008 Workshop: Manifolds in Medical Imaging: Metrics, Learning and Beyond

Issue

In modern medical image data, manifolds arise at varying scales. At one extreme, complete 3D data sets lie along manifolds parameterized by (for example) patient breathing and heartbeat patterns, or by confounding variables such as parameters or templates used in an image warping algorithm. At the other extreme, measurements taken at each voxel in multi-parametric MR images lie along locally defined manifolds that reflect nonlinear relationships among various MRI measurements on a voxel.

Discovering, visualizing and exploiting the structure of these manifolds supports the ability to select image-derived attributes that are informed by the structure of the underlying manifold. This offers fundamentally new tools for image registration, segmentation, visualization, reconstruction, and classification of data volumes. This collection brings together research in computer science, applied mathematics, statistics and medical imaging. Organizers / Associate Editors: Robert Pless, Washington University in St. Louis Christos Davatzikos, University of Pennsylvania

Title

Authors

Keywords

Combinatorial Ricci Curvature for Image Processing

Saucan, Emil, Appleboim, Eli, Wolansky, Gershon, Zeevi, Yehoshua

Ricci, Curvature

On What Manifold do Diffusion Tensors Live?

Pasternak, Ofer, Verma, Ragini, Sochen, Nir, Basser, Peter J.

DTI, Manifold

The Labeling of Cortical Sulci using Multidimensional Scaling

Srivastava, Anuj, Barillot, Christian, Mani, Meena

labeling, cortical

Construction of Statistical Shape Models from Minimal Deformations

Zikic, Darko, Glocker, Ben, Sass Hansen, Micheal, Khamene, Ali, Navab, Nassir

Shape Models, Statistics

Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas

Wassermann , Demian, Deriche, Rachid

clustering, learning

1-5 of 6