title: Fast shape-based nearest-neighbor search for brain MRIs using hierarchical feature matching.
abstract: This paper presents a fast method for quantifying shape differences/similarities between pairs of magnetic resonance (MR) brain images. Most shape comparisons in the literature require some kind of deformable registration or identification of exact correspondences. The proposed approach relies on an optimal matching of a large collection of features, using a very fast, hierarchical method from the literature, called spatial pyramid matching (SPM). This paper shows that edge-based image features in combination with SPM results in a fast similarity measure that captures relevant anatomical information in brain MRI. We present extensive comparisons against known methods for shape-based, k-nearest-neighbor lookup to evaluate the performance of the proposed method. Finally, we show that the method compares favorably with more computation-intensive methods in the construction of local atlases for use in brain MR image segmentation.
authors: Scientific Computing and Imaging Institute, University of Utah, USA.
authors: Peihong
authors: P
authors: Zhu
authors: Suyash P
authors: SP
authors: Awate
authors: Samuel
authors: S
authors: Gerber
authors: Ross
authors: R
authors: Whitaker
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009415:Nerve Net; D010363:Pattern Recognition, Automated / Q000379:methods; D013382:Subtraction Technique