Automatic registration of vestibular systems with exact landmark correspondence

被引:5
作者
Zhang, Minqi [1 ]
Li, Fang [1 ]
Wang, Xingce [2 ]
Wu, Zhongke [2 ]
Xin, Shi-Qing [3 ]
Lui, Lok-Ming [4 ]
Shi, Lin [5 ]
Wang, Defeng [5 ]
He, Ying [1 ]
机构
[1] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
[2] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China
[3] Ningbo Univ, Fac Informat Sci & Engn, Ningbo, Zhejiang, Peoples R China
[4] Chinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
[5] Chinese Univ Hong Kong, Dept Imaging & Intervent Radiol, Hong Kong, Hong Kong, Peoples R China
关键词
Registration; Landmark matching; Vestibular system; Discrete geodesic; Harmonic map; Holomorphic; 1-form;
D O I
10.1016/j.gmod.2014.04.010
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Shape registration has a wide range of applications in geometric modeling, medical imaging, and computer vision. This paper focuses on the registration of the genus-3 vestibular systems and studies the geometric differences between the normal and Adolescent Idiopathic Scoliosis (AIS) groups. The non-trivial topology of the VS poses great technical challenges to the geometric analysis. To tackle these challenges, we present an effective and practical solution to register the vestibular systems. We first extract six geodesic landmarks for the VS, which are stable, intrinsic, and insensitive to the VS's resolution and tessellation. Moreover, they are highly consistent regardless of the AIS and normal groups. The detected geodesic landmarks partition the VS into three patches, a topological annulus and two topological disks. For each pair of patches of the AIS subject and the control, we compute a bijective map using the holomorphic 1-form and harmonic map techniques. With a carefully designed boundary condition, the three individual maps can be glued in a seamless manner so that the resulting registration is a homeomorphism with exact landmark matching. Our method is robust, automatic and efficient. It takes only a few seconds on a low-end PC, which significantly outperforms the non-rigid ICP algorithm. We conducted a student's t-test on the test data. Computational results show that using the mean curvature measure E-H, our method can distinguish the AIS subjects and the normal subjects. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:532 / 541
页数:10
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