Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A Variational Framework for Joint Segmentation and Registration
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Multiscale Joint Segmentation and Registration of Image Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
Robust Brain Registration Using Adaptive Probabilistic Atlas
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Segmentation of sub-cortical structures by the graph-shifts algorithm
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Effects of registration regularization and atlas sharpness on segmentation accuracy
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Joint registration and segmentation of dynamic cardiac perfusion images using MRFs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Simultaneous registration and segmentation of anatomical structures from brain MRI
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A unifying approach to registration, segmentation, and intensity correction
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Shape based segmentation of anatomical structures in magnetic resonance images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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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The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. We aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a Maximum a Posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.