Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Manifold learning for biomarker discovery in MR imaging
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Robust atlas-based segmentation of highly variable anatomy: left atrium segmentation
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Combining morphological information in a manifold learning framework: application to neonatal MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Construction of neuroanatomical shape complex atlas from 3D brain MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units
EG PGV'09 Proceedings of the 9th Eurographics conference on Parallel Graphics and Visualization
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
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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.