An automated pipeline for cortical sulcal fundi extraction

被引:33
作者
Li, Gang [3 ]
Guo, Lei [3 ]
Nie, Jingxin [3 ]
Liu, Tianming [1 ,2 ]
机构
[1] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA
[2] Univ Georgia, Bioimaging Res Ctr, Athens, GA 30602 USA
[3] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China
关键词
Sulcal fundi extraction; Cortical surface; Geodesic path; Fast marching on manifold; Maximum principal curvature; SURFACE; REGISTRATION; IMAGES; VARIABILITY; FRAMEWORK; CURVES; RECONSTRUCTION; SEGMENTATION; PARCELLATION; GENERATION;
D O I
10.1016/j.media.2010.01.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a novel automated pipeline for extraction of sulcal fundi from triangulated cortical surfaces. This method consists of four consecutive steps. Firstly, we adopt a finite difference method to estimate principal curvatures, principal directions and curvature derivatives, along the principal directions, for each vertex. Then, we detect the sulcal fundi segment in each triangle of the cortical surface based on curvatures and curvature derivatives. Afterwards, we link the sulcal fundi segments into continuous curves. Finally, we connect breaking sulcal fundi and smooth bumping sulcal fundi by using the fast marching method on the cortical surface. The proposed method can find the accurate sulcal fundi using curvatures and curvature derivatives without any manual interaction. The method was applied to 10 normal brain MR images on inner cortical surfaces. We quantitatively evaluated the accuracy of the sulcal fundi extraction method using manually labeled sulcal fundi by experts. The average difference between automatically extracted major sulcal fundi and the expert labeled results is consistently around 1.0 mm on 10 subject images, indicating the good performance of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:343 / 359
页数:17
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