V–bundles: Clustering fiber trajectories from diffusion MRI in linear time

Abstract

Fiber clustering algorithms are employed to find patterns in the structural connections of the human brain as traced by tractography algorithms. Current clustering algorithms often require the calculation of large similarity matrices and thus do not scale well for datasets beyond 100,000 streamlines. We extended and adapted the 2D vector field k– means algorithm of Ferreira et al. to find bundles in 3D tractography data from diffusion MRI (dMRI) data. The resulting algorithm is linear in the number of line segments in the fiber data and can cluster large datasets without the use of random sampling or complex multipass procedures. It copes with interrupted streamlines and allows multisubject comparisons.