Unsupervised white matter fiber tracts clustering methodology with application on brain MRI data

Understanding the geometrical organization of the white matter fibers is one of the current challenges in neuroimaging. White matter fiber clustering technique appears to a corner stone to solve this problem. In this paper, we propose a rapid and efficient unsupervised white matter fiber tracts clustering methodology based on a novel fiber tract similarity metric and an approximation of the k-means algorithm. In this approach, we first define a distance metric capable to quantify the intrinsic geometry of the fiber tracts. This metric is based on a combination of the symmetric Chamfer distance and mean local orientation measures between fiber tracts. Second, we perform the randomized feature selection algorithm proposed for the k-means problem to reduce the dimensionality of the distance data matrix generated from all the fiber tracts using the defined metric. The k-means algorithm is then performed on the reduced distance matrix to cluster the fiber tracts. Finally, we evaluate the method on the synthetic data and in vivo adult brain dataset.