Automatic clustering of white matter fibers based on symbolic sequence analysis

Abstract

Fiber clustering is a very important step towards tract-based, quantitative analysis of white matter via diffusion tensor imaging (DTI). This work proposes a new computational framework for white matter fiber clustering based on symbolic sequence analysis method. We first perform brain tissue segmentation on the DTI image using a multi-channel fusion method and parcellate the whole brain into anatomically labeled regions via a hybrid volumetric and surface warping algorithm. Then, we perform standard fiber tractography on the DTI image and encode each tracked fiber by a sequence of labeled brain regions. Afterwards, the similarity between any pair of anatomically encoded fibers is defined as the similarity of symbolic sequences, which is a well-studied problem in the bioinformatics domain such as is used for gene and protein symbolic sequences comparisons. Finally, the normalized graph cut algorithm is applied to cluster the fibers into bundles based on the above defined similarities between any pair of fibers. Our experiments show promising results of the proposed fiber clustering framework.