A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
A Curve Evolution Approach to Medical Image Magnification via the Mumford-Shah Functional
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Nonparametric shape priors for active contour-based image segmentation
Signal Processing
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled shape distribution-based segmentation of multiple objects
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A general framework for image segmentation using ordered spatial dependency
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Statistics of pose and shape in multi-object complexes using principal geodesic analysis
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
IEEE Transactions on Image Processing
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We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain 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 inter-shape (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 based on training data, 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 (MR) images and present a quantitative performance analysis. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy.