Conference Paper

Automatically Learning Cortical Folding Patterns.

Centre for Intell. Machines, McGill Univ., Montreal, QC, Canada
DOI: 10.1109/ISBI.2009.5193310 Conference: Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009
Source: IEEE Xplore


A data-driven technique is presented for automatically learning cortical folding patterns from MR brain images of different subjects. Cortical patterns are represented in terms of generic scale-invariant image features. Learning automatically identifies a set of features that occur with statistical regularity in appearance and geometry from a large set of MR volume renderings, based on a predescribed anatomical region of interest. A filtering technique is presented for distinguishing between valid cortical features and those likely to arise from incorrect correspondences, based on feature geometry. Expert validation of 100 feature instances shows that 77% correctly identify the same underlying cortical structure in different brains despite high inter-subject variability, and filtering improves the ability to identify the most meaningful patterns.

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    • "where G(x, σ) is a Gaussian kernel of mean x and variance σ, and σ 0 represents the scale of the original image. The Gaussian scale-space arises as the solution to the heat equation when the image is modeled as a diffusion process (Koenderink, 1984), and can be derived from a 2 M. Toews et al. / NeuroImage xxx (2009) xxx–xxx "
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