A landmark-based brain conformal parametrization with automatic landmark tracking technique

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

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

Abstract

In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Our algorithm obtains a hypothesized landmark curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages308-315
Number of pages8
Volume4191 LNCS - II
StatePublished - 2006
Externally publishedYes
Event9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark
Duration: Oct 1 2006Oct 6 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4191 LNCS - II
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
CountryDenmark
CityCopenhagen
Period10/1/0610/6/06

Fingerprint

Landmarks
Parametrization
Brain
Curve
Principal direction
Parameterization
Anchors
Location Parameter
Experimental Results
Partial differential equations
Tracing
Segmentation
Partial differential equation
Grid
Direction compound

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Lui, L. M., Wang, Y., Chan, T. F., & Thompson, P. M. (2006). A landmark-based brain conformal parametrization with automatic landmark tracking technique. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4191 LNCS - II, pp. 308-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4191 LNCS - II).

A landmark-based brain conformal parametrization with automatic landmark tracking technique. / Lui, Lok Ming; Wang, Yalin; Chan, Tony F.; Thompson, Paul M.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4191 LNCS - II 2006. p. 308-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4191 LNCS - II).

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

Lui, LM, Wang, Y, Chan, TF & Thompson, PM 2006, A landmark-based brain conformal parametrization with automatic landmark tracking technique. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4191 LNCS - II, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4191 LNCS - II, pp. 308-315, 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, Copenhagen, Denmark, 10/1/06.
Lui LM, Wang Y, Chan TF, Thompson PM. A landmark-based brain conformal parametrization with automatic landmark tracking technique. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4191 LNCS - II. 2006. p. 308-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Lui, Lok Ming ; Wang, Yalin ; Chan, Tony F. ; Thompson, Paul M. / A landmark-based brain conformal parametrization with automatic landmark tracking technique. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4191 LNCS - II 2006. pp. 308-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{d067293f1734453fb733e02db714aa81,
title = "A landmark-based brain conformal parametrization with automatic landmark tracking technique",
abstract = "In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Our algorithm obtains a hypothesized landmark curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.",
author = "Lui, {Lok Ming} and Yalin Wang and Chan, {Tony F.} and Thompson, {Paul M.}",
year = "2006",
language = "English (US)",
isbn = "354044727X",
volume = "4191 LNCS - II",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "308--315",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - A landmark-based brain conformal parametrization with automatic landmark tracking technique

AU - Lui, Lok Ming

AU - Wang, Yalin

AU - Chan, Tony F.

AU - Thompson, Paul M.

PY - 2006

Y1 - 2006

N2 - In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Our algorithm obtains a hypothesized landmark curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.

AB - In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Our algorithm obtains a hypothesized landmark curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.

UR - http://www.scopus.com/inward/record.url?scp=33750259117&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750259117&partnerID=8YFLogxK

M3 - Conference contribution

SN - 354044727X

SN - 9783540447276

VL - 4191 LNCS - II

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 308

EP - 315

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -