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Revision as of 04:21, 9 December 2011
Home < Projects:AtlasBasedBrainSegmentationBack to Utah Algorithms
Atlas-Based Brain Segmentation
Description
Atlases are widely used to aid in Brain segmentation or tissue classification, they are typically constructed by summarizing information concerning image intensities and anatomical shapes from a set of training images that includes manual segmentations. Conventional atlas building methods rely on the averaging process of all training images, which can lead to significant loss of information. Recent research shows that selecting a subset of atlases that are similar to the test subject provides more accurate segmentation than those given by randomly selected atlases.
We proposed a fast,shape-based hierarchical matching approach based on Spatial Pyramid Matching (SPM) to perform a very efficient k-NN search for testing brains. We comparied the proposed method against known methods for k-NN lookup and evaluated the atlases built on these methods on a training set contains 259 brain MRIs and a testing set of 20 brain MRIs.
Key Investigators
- Utah: Peihong Zhu, Suyash P. Awate, Ross Whitaker
Publications
- P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.
