A Geometric Method for Automatic Extraction of Sulcal Fundi

Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fund...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2007-04, Vol.26 (4), p.530-540
Main Authors: Chiu-Yen Kao, Hofer, M., Sapiro, G., Stern, J., Rehm, K., Rottenberg, D.A.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Brain
Brain imaging
Brain research
brain warping
Cerebral cortex
Cerebral Cortex - anatomy & histology
Computation
Data mining
Extraction
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Joining processes
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
MRI
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
Representations
Reproducibility of Results
Sensitivity and Specificity
Software algorithms
Software packages
Spline
Splines
Studies
sulcal fundi
surface splines
thinning
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Summary:Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangular mesh representing the cortical surface. The input to our algorithm is a triangular mesh representation of an extracted cortical surface as computed by one of several available software packages for performing automated and semi-automated cortical surface extraction. Given this input we first compute a geometric depth measure for each triangle on the cortical surface mesh, and based on this information we extract sulcal regions by checking for connected regions exceeding a depth threshold. We then identify endpoints of each region and delineate the fundus by thinning the connected region while keeping the endpoints fixed. The curves, thus, defined are regularized using weighted splines on the surface mesh to yield high-quality representations of the sulcal fundi. We present the geometric framework and validate it with real data from human brains. Comparisons with expert-labeled sulcal fundi are part of this validation process
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.886810