DEFORMOTION: Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Registration Assisted Image Smoothing and Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
MAP MRF Joint Segmentation and Registration
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Knowledge-based Registration & Segmentation of the Left Ventricle: A Level Set Approach
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
A Variational Framework for Joint Segmentation and Registration
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Journal of Mathematical Imaging and Vision
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
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
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A combined segmentation and registration framework with a nonlinear elasticity smoother
Computer Vision and Image Understanding
Joint co-segmentation and registration of 3D ultrasound images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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In this paper, we present a novel variational formulation of the registration assisted image segmentation problem which leads to solving a coupled set of nonlinear PDEs that are solved using efficient numerical schemes. Our work is a departure from earlier methods in that we have a unified variational principle wherein non-rigid registration and segmentation are simultaneously achieved; unlike previous methods of solution for this problem, our algorithm can accommodate for image pairs having very distinct intensity distributions. We present examples of performance of our algorithm on synthetic and real data sets along with quantitative accuracy estimates of the registration.