Summary: 1838 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 7, JULY 2009
A Hybrid Geometric­Statistical Deformable Model
for Automated 3-D Segmentation in Brain MRI
Albert Huang*, Student Member, IEEE, Rafeef Abugharbieh, Member, IEEE, Roger Tam,
and Alzheimer's Disease Neuroimaging Initiative
Abstract--We present a novel 3-D deformable model-based ap-
proach for accurate, robust, and automated tissue segmentation of
brain MRI data of single as well as multiple magnetic resonance
sequences. The main contribution of this study is that we em-
ploy an edge-based geodesic active contour for the segmentation
task by integrating both image edge geometry and voxel statistical
homogeneity into a novel hybrid geometric­statistical feature to
regularize contour convergence and extract complex anatomical
structures. We validate the accuracy of the segmentation results
on simulated brain MRI scans of both single T1-weighted and
multiple T1/T2/PD-weighted sequences. We also demonstrate the
robustness of the proposed method when applied to clinical brain
MRI scans. When compared to a current state-of-the-art region-
based level-set segmentation formulation, our white matter and
gray matter segmentation resulted in significantly higher accuracy

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

Collections: Biology and Medicine; Computer Technologies and Information Sciences