Network connectivity via inference over curvature-regularizing line graphs

Authors:
Maxwell D. Collins;Vikas Singh;Andrew L. Alexander
Affiliations:
Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI;Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI;Waisman Laboratory for Brain Imaging, Departments of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Year:
2010

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Abstract

Diffusion Tensor Imaging (DTI) provides estimates of local directional information regarding paths of white matter tracts in the human brain. An important problem in DTI is to infer tract connectivity (and networks) from given image data. We propose a method that infers high-level network structures and connectivity information from Diffusion Tensor images. Our algorithm extends principles from perceptual contours to construct a weighted line-graph based on how well the tensors agree with a set of proposal curves (regularized by length and curvature). The problem of extracting high-level anatomical connectivity is then posed as an optimization problem over this curvature-regularizing graph - which gives subgraphs which comprise a representation of the tracts' network topology. We present experimental results and an open-source implementation of the algorithm.