Multiscale Joint Segmentation and Registration of Image Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Automatic segmentation of bladder and prostate using coupled 3D deformable models
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A review of atlas-based segmentation for magnetic resonance brain images
Computer Methods and Programs in Biomedicine
A combined segmentation and registration framework with a nonlinear elasticity smoother
Computer Vision and Image Understanding
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
A unified framework for segmentation-assisted image registration
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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In this paper we present coupled partial differential equations (PDEs) for the problem of joint segmentation and registration. The registration component of the method estimates a deformation field between boundaries of two structures. The desired coupling comes from two PDEs that estimate a common surface through segmentation and its non-rigid registration with a target image. The solutions of these two PDEs both decrease the total energy of the surface, and therefore aid each other in finding a locally optimal solution. Our technique differs from recently popular joint segmentation and registration algorithms, all of which assume a rigid transformation among shapes. We present both the theory and results that demonstrate the effectiveness of the approach.