Title :
Locally-Constrained Region-Based Methods for DW-MRI Segmentation
Author :
Melonakos, John ; Niethammer, Marc ; Mohan, Vandana ; Kubicki, Marek ; Miller, James V. ; Tannenbaum, Allen
Author_Institution :
Georgia Inst. of Technol., Atlanta
Abstract :
In this paper, we describe a method for segmenting fiber bundles from diffusion-weighted magnetic resonance images using a locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those voxels which are locally connected to the fiber bundle. Rather than attempting to find large numbers of open curves or single fibers, which individually have questionable meaning, this method segments the full fiber bundle region. The strengths of this approach include its ease-of-use, computational speed, and applicability to a wide range of fiber bundles. In this work, we show results for segmenting the cingulum bundle. Finally, we explain how this approach and extensions thereto overcome a major problem that typical region-based flows experience when attempting to segment neural fiber bundles.
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; DW-MRI segmentation; diffusion-weighted magnetic resonance image; locally-constrained region based approach; neural fiber bundle; Bayesian methods; Biomedical computing; Biomedical imaging; Decision making; Hospitals; Image segmentation; Magnetic resonance; Signal to noise ratio; Stochastic resonance; Tensile stress;
Conference_Titel :
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-1-4244-1630-1
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2007.4409167