Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89396
Title: Automatic registration of vestibular systems with exact landmark correspondence
Authors: Zhang, Minqi
Li, Fang
Wang, Xingce
Wu, Zhongke
Xin, Shi-Qing
Lui, Lok-Ming
Shi, Lin
Wang, Defeng
He, Ying
Keywords: Registration
Landmark Matching
DRNTU::Engineering::Computer science and engineering
Issue Date: 2014
Source: Zhang, M., Li, F., Wang, X., Wu, Z., Xin, S. Q., Lui, L. M., ... He, Y. (2014). Automatic registration of vestibular systems with exact landmark correspondence. Graphical Models, 76(5), 532-541. doi:10.1016/j.gmod.2014.04.010
Series/Report no.: Graphical Models
Abstract: 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 EH, our method can distinguish the AIS subjects and the normal subjects.
URI: https://hdl.handle.net/10356/89396
http://hdl.handle.net/10220/46233
ISSN: 1524-0703
DOI: 10.1016/j.gmod.2014.04.010
Rights: © 2014 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Graphic Models, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.gmod.2014.04.010].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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