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Diffusion magnetic resonance imaging (dMRI) data allow to reconstruct the 3Dpathways of axons within the white matter of the brain as a tractography. Theanalysis of tractographies has drawn attention from the machine learning andpattern recognition communities providing novel challenges such as finding anappropriate representation space for the data. Many of the current learningalgorithms require the input to be from a vectorial space. This requirementcontrasts with the intrinsic nature of the tractography because its basicelements, called streamlines or tracks, have different lengths and differentnumber of points and for this reason they cannot be directly represented in acommon vectorial space. In this work we propose the adoption of thedissimilarity representation which is an Euclidean embedding technique definedby selecting a set of streamlines called prototypes and then mapping any newstreamline to the vector of distances from prototypes. We investigate thedegree of approximation of this projection under different prototype selectionpolicies and prototype set sizes in order to characterise its use ontractography data. Additionally we propose the use of a scalable approximationof the most effective prototype selection policy that provides fast andaccurate dissimilarity approximations of complete tractographies.

The Approximation of the Dissimilarity Projection