Automatic Landmark and Its Application to the Optimization of Brain Conformal Mapping
Lok Ming Lui, Yalin Wang, Tony F. Chan, and Paul M. Thompson
Anatomical features on the cortical surface 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 the cortical surfaces
automatically based on the principal directions. Suppose we are
given the global conformal parametrization of the cortical surface.
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.
Figures (click on each for a larger version):
L.M. Lui, Y. Wang, T.F. Chan, and P.M. Thompson,
"Brain Anatomical Feature Detection by Solving Partial Differential
Equations on General Manifolds", Discrete and Continuous Dynamical Systems B, 7(3), May 2007, pp. 605-618
L. M. Lui, Y. Wang, T.F. Chan and P.M. Thompson,
Landmark Tracking and Its Application to the Optimization of Brain Conformal Mapping",
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, Jun. 2006, Vol. 2, pp. 1784-1792