Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatically learning cortical folding patterns
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The variability in cortical morphology across subjects makes it difficult to develop a general atlas of cortical sulci. In this paper, we present a data-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be present in all subjects. The choice of feature type (SIFT, KLT, Harris-affine) is explored with regards to identifying cortical folding patterns while also uncovering their group-related variability across subjects. The model is built on lateral volume renderings from the ICBM dataset, and applied to hemisphere classification in order to identify patterns of lateralization based on each feature type.