Journal of Mathematical Imaging and Vision
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
Extracting Tractosemas from a Displacement Probability Field for Tractography in DW-MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Symmetric positive 4th order tensors & their estimation from diffusion weighted MRI
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Multi-fiber reconstruction from diffusion MRI using mixture of wisharts and sparse deconvolution
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Geodesic-loxodromes for diffusion tensor interpolation and difference measurement
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Fast and simple calculus on tensors in the log-euclidean framework
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Maximum entropy spherical deconvolution for diffusion MRI
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data. This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed.