Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation.
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.
Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey. uzunbas@cs.rutgers.edu
Mustafa Gökhan
MG
Uzunbaş
Octavian
O
Soldea
Devrim
D
Unay
Müjdat
M
Cetin
Gözde
G
Unal
Aytül
A
Erçil
Ahmet
A
Ekin
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D016477:Artifacts; D001479:Basal Ganglia / Q000033:anatomy & histology*; D001921:Brain / Q000033:anatomy & histology; D002648:Child; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D018709:Statistics, Nonparametric