Robust Fiber Clustering of Cerebral Fiber Bundles in White Matter

Diffusion tensor imaging fiber tracking (DTI-FT) has been widely accepted in the diagnosis and treatment of brain diseases. During the rendering pipeline of specific fiber tracts, the image noise and low resolution of DTI would lead to false propagations. In this paper, we propose a robust fiber clustering (FC) approach to diminish false fibers from one fiber tract. Our algorithm consists of three steps. Firstly, the optimized fiber assignment continuous tracking (FACT) is implemented to reconstruct one fiber tract; and then each curved fiber in the fiber tract is mapped to a point by kernel principal component analysis (KPCA); finally, the point clouds of fiber tract are clustered by hierarchical clustering which could distinguish false fibers from true fibers in one tract. In our experiment, the corticospinal tract (CST) in one case of human data in vivo was used to validate our method. Our method showed reliable capability in decreasing the false fibers in one tract. In conclusion, our method could effectively optimize the visualization of fiber bundles and would help a lot in the field of fiber evaluation.