Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm
作者：
Highlights：
• A novel segmentation algorithm for white matter tissues using DTI data is proposed.
• This segmentation method is based on a new clustering algorithm called NDEC.
• NDEC does not require number of clusters as a priori.
• The performance of NDEC is compared with other clustering algorithms.
• NDEC obtained best performance using Johns Hopkins University DTI data.
摘要
•A novel segmentation algorithm for white matter tissues using DTI data is proposed.•This segmentation method is based on a new clustering algorithm called NDEC.•NDEC does not require number of clusters as a priori.•The performance of NDEC is compared with other clustering algorithms.•NDEC obtained best performance using Johns Hopkins University DTI data.
论文关键词：Density-based clustering algorithm,Chameleon,DBSCAN,Spectral clustering,White matter fiber bundles segmentation
论文评审过程：Received 25 March 2016, Revised 15 August 2016, Accepted 29 September 2016, Available online 30 September 2016, Version of Record 5 October 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.09.003
