On the differential geometry of 3D flow patterns: Generalized helicoids and diffusion MRI analysis

被引:0
|
作者
Savadjiev, Peter [1 ]
Zucker, Steven W. [2 ]
Siddiqi, Kaleem [1 ]
机构
[1] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada
[2] Yale Univ, Dept Comp Sci, New Haven, CT USA
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Configurations of dense locally parallel 3D curves occur in medical imaging, computer vision and graphics. Examples include white matter fibre tracts, textures, fur and hair We develop a differential geometric characterization of such structures by considering the local behaviour of the associated 3D frame field, leading to the associated tangential, normal and bi-normal curvature functions. Using results from the theory of generalized minimal surfaces we adopt a generalized helicoid model as an osculating object and develop the connection between its parameters and these curvature functions. These developments allow for the construction of parametrized 3D vector fields (sampled osculating objects) to locally approximate these patterns. We apply these results to the analysis of diffusion MRI data via a type of 3D streamline flow. Experimental results on data from a human brain demonstrate the advantages of incorporating the full differential geometry.
引用
收藏
页码:2017 / 2024
页数:8
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