| Popis: |
Rich information about brain tissue microstructure and composition is yielded by MRI-based measurement of the local diffusion tensor (DT) of water molecules in neural fibers, whose axons are running in myelinated fiber tracts. Diffusion tensor imaging (DTI) possesses high-dimensional and complex structure, so that detecting available pattern information and its analysis based on conventional linear statistics and classification methods become inefficient. Classification, segmentation, compression or visualization of the data could be facilitated through dimension reduction. The previously proposed methods mostly rely on complex low dimensional manifold embedding of the high-dimensional space, which are not able to deal with complex and high dimensional data. The purpose of this paper is to propose a new method for meaningful visualization of brain white matter using diffusion tensor data to map the six-dimensional tensor to a three dimensional space, employing Markov random walk and diffusion distance algorithms, leading to a new distance-preserving map for the DTI data with lower dimension and higher throughput information. |
