Symmetrical Dense Optical Flow Estimation with Occlusions Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Consistent multi-modal non-rigid registration based on a variational approach
Pattern Recognition Letters
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Stochastic inverse consistency in medical image registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Local joint entropy based non-rigid multimodality image registration
Pattern Recognition Letters
Hi-index | 0.00 |
This paper presents a novel variational model for inverse consistent deformable image registration. This model deforms the source and target image simultaneously, and aligns the deformed source and deformed target images in the way that the both transformations are inverse consistent. The model does not computes the inverse transforms explicitly, alternatively it finds two more deformation fields satisfying the invertibility constraints. Moreover, to improve the robustness of the model to noises and the choice of parameters, the dissimilarity measure is derived from the likelihood estimation of the residue image. The proposed model is formulated as an energy minimization problem, which involves the regularization for four deformation fields, the dissimilarity measure of the deformed source and deformed target images, and the invertibility constraints. The experimental results on clinical data indicate the efficiency of the proposed method, and improvements in robustness, accuracy and inverse consistency.