Automatic landmark tracking and its application to the optimization of brain conformal mapping

Lok Ming Lui, Yalin Wang, Tony F. Chan, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Anatomical features on cortical surfaces are usually represented by landmark curves, called sulci/gyri curves. These landmark curves are important information for neuroscientists to study brain diseases and to match different cortical surfaces. Manual labelling of these landmark curves is time-consuming, especially when there is a large set of data. In this paper, we proposed to trace the landmark curves on cortical surfaces automatically based on the principal directions. Suppose we are given the global conformal parametrization of a cortical surface, By fixing two endpoints, the anchor points, we propose to trace the landmark curves iteratively on the spherical/rectangular parameter domain along the principal direction. Consequently, the landmark curves can be mapped onto the cortical surface. To speed up the iterative scheme, a good initial guess of the landmark curve is necessary. We proposed a method to get a good initialization by extracting the high curvature region on the cortical surface using the Chan-Vese segmentation. This involves solving a PDE on the manifold using our global conformal parametrization technique. Experimental results show that the landmark curves detected by our algorithm closely resemble to those manually labelled curves. As an application, we used these automatically labelled landmark curves to build average cortical surfaces with an optimized brain conformal mapping method. Experimental results show our method can help automatically matching brain cortical surfaces.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages1784-1789
Number of pages6
Volume2
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
CountryUnited States
CityNew York, NY
Period6/17/066/22/06

Fingerprint

Conformal mapping
Brain
Anchors
Labeling

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Lui, L. M., Wang, Y., Chan, T. F., & Thompson, P. M. (2006). Automatic landmark tracking and its application to the optimization of brain conformal mapping. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 1784-1789). [1640970] https://doi.org/10.1109/CVPR.2006.67

Automatic landmark tracking and its application to the optimization of brain conformal mapping. / Lui, Lok Ming; Wang, Yalin; Chan, Tony F.; Thompson, Paul M.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 2006. p. 1784-1789 1640970.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lui, LM, Wang, Y, Chan, TF & Thompson, PM 2006, Automatic landmark tracking and its application to the optimization of brain conformal mapping. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2, 1640970, pp. 1784-1789, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, New York, NY, United States, 6/17/06. https://doi.org/10.1109/CVPR.2006.67
Lui LM, Wang Y, Chan TF, Thompson PM. Automatic landmark tracking and its application to the optimization of brain conformal mapping. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2. 2006. p. 1784-1789. 1640970 https://doi.org/10.1109/CVPR.2006.67
Lui, Lok Ming ; Wang, Yalin ; Chan, Tony F. ; Thompson, Paul M. / Automatic landmark tracking and its application to the optimization of brain conformal mapping. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 2006. pp. 1784-1789
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