Committee: Hank Childs (chair), Allen Malony, Kent Stevens
Directed Research Project(Jun 2015)
Keywords: Tractography Visualization Clustering
We present a novel methodology for clustering and visualizing large-scale tractography data sets. Tractography data sets are very large, containing up to hundreds of millions of tracts; making visualizing and understanding this data very difficult. Our method reduces and simplifies this data to create coherent groupings and visualizations. Our input is a collection of tracts, from which we derive metrics and perform a k-means++ clustering. Using the clustered data, we create a binning volume that contains the counts of the number of tracts that intersect each bin, from which we can perform standard visualization techniques. Our contribution is the visualization technique and methodology itself, as well as an extensive study and evaluation schema. Our study utilizes our evaluation schema to identify the best and most influential clustering metrics in a metric set, and an optimal number of clusters under varying user requirements.