NDLI: Area Preserving Brain Mapping

Content Provider	IEEE Xplore Digital Library
Author	Zhengyu Su Wei Zeng Rui Shi Yalin Wang Jian Sun Xianfeng Gu
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA (Zhengyu Su; Rui Shi; Xianfeng Gu) \|\| Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA (Wei Zeng) \|\| Sch. of Comput., Arizona State Univ., Tempe, AZ, USA (Yalin Wang) \|\| Math. Sci. Center, Tsinghua Univ., Beijing, China (Jian Sun)
Abstract	Brain mapping transforms the brain cortical surface to canonical planar domains, which plays a fundamental role in morphological study. Most existing brain mapping methods are based on angle preserving maps, which may introduce large area distortions. This work proposes an area preserving brain mapping method based on Monge-Brenier theory. The brain mapping is intrinsic to the Riemannian metric, unique, and diffeomorphic. The computation is equivalent to convex energy minimization and power Voronoi diagram construction. Comparing to the existing approaches based on Monge-Kantorovich theory, the proposed one greatly reduces the complexity (from $n^{2}$ unknowns to n ), and improves the simplicity and efficiency. Experimental results on caudate nucleus surface mapping and cortical surface mapping demonstrate the efficacy and efficiency of the proposed method. Conventional methods for caudate nucleus surface mapping may suffer from numerical instability, in contrast, current method produces diffeomorpic mappings stably. In the study of cortical surface classification for recognition of Alzheimer's Disease, the proposed method outperforms some other morphometry features.
Starting Page	2235
Ending Page	2242
File Size	973107
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.290
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surface morphology Conformal mapping Shape Vectors Brain mapping Alzheimer's disease
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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