A Novel White Matter Fibre Tracking Algorithm Using Probabilistic Tractography and Average Curves
This paper presents a novel white matter fibre tractography approach using average curves of probabilistic fibre tracking measures. We compute “representative curves from the original probabilistic curve-set using two different averaging methods. These typical curves overcome a number of the limitations of deterministic and probabilistic approaches. They produce strong connections to every anatomically distinct fibre tract from a seed point and also convey important information about the underlying probability distribution. A new clustering algorithm is employed to separate fibres into branches before applying averaging methods. The performance of the technique is verified on a wide range of seed points using a phantom dataset and an in vivo dataset.
Nagulan Ratnarajah Andrew Simmons Oleg Davydov Ali Hojjatoleslami
Medical Image Computing, School of BioSciences, University of Kent, U.K Neuroimaging Department, Institute of Psychiatry, Kings College London, U.K.NIHR Biomedical Research Department of Mathematics and Statistics, University of Strathclyde, Glasgow, U.K. Medical Image Computing, School of BioSciences, University of Kent, U.K.
国际会议
北京
英文
666-673
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）