In earlier work [
1], we demonstrated that cortical
registration could be improved on simulated data by using blurred,
geometric, image-based features (
Lvv) or explicitly extracted and
blurred sulcal traces on simulated data. Here, the technique is modified
to incorporate sulcal ribbons in conjunction with a chamfer distance
objective function to improve registration in real MRI data as well.
Experiments with 10 simulated data sets demonstrate a 56% reduction in
residual sulcal registration error (from 3.4 to 1.5mm, on average) when
compared to automatic linear registration and an 28% improvement over
our previously published non-linear technique (from 2.1 to 1.5mm). The
simulation results are confirmed by experiments with real MRI data from
young normal subjects, where sulcal misregistration is reduced by 20%
(from 5.0mm to 4.0mm) and 11% (from 4.5 to 4.0mm) over the standard
linear and nonlinear registration methods, respectively.
