A Sobolev Norm Based Distance Measure for HARDI Clustering A Feasibility Study on Phantom and Real Data

摘要：

Dissimilarity measures for DTI clustering are abundant.However, for HARDI, the L2 norm has up to now been one of only few practically feasible measures. In this paper we propose a new measure, that not only compares the amplitude of diffusion profiles, but also rewards coincidence of the extrema. We tested this on phantom and real brain data. In both cases, our measure significantly outperformed the L2 norm.

作者: Ellen Brunenberg Remco Duits Bart ter Haar Romeny Bram Platel

作者单位: Biomedical Engineering, Eindhoven University of Technology Biomedical Engineering, Eindhoven University of TechnologyMathematics and Computer Science, Eindhove Biomedical Engineering, Maastricht University Medical Center

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 175-182

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）