Identifying fiber bundles with regularised к-means clustering applied to the grid-based data

Brain segmentation represents a very complex and challenging problem. Fiber pathways connecting the same functional regions of the brain form a natural anatomical group (bundle). Fiber bundling is a typical clustering problem. Note that the fiber bundles in the human brain take various sizes and shapes. The measure used to define the spatial proximity between curves is of fundamental importance for clustering. It is not easy (first of all in terms of the computational time) to compare different fibers directly taking into account that they have different lengths and structures. As a solution to this problem, we propose to consider intermediate key-sets with several very important 3D-points. Depending on the proximity to one particular set we can make a conclusion whether or not two different curves are similar. Our method was tested successfully during the International 2009 Pittsburgh Brain Connectivity IEEE ICDM Competition, where we achieved the top score in Challenge 1 (our score was 50.49% higher compared to the second highest score). Also, we were placed second in Challenge 2.