Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
A novel consistency regularizer for meshless nonrigid image registration
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
A meshless method for variational nonrigid 2-D shape registration
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
An information-theoretic method for multimodality medical image registration
Expert Systems with Applications: An International Journal
Multimodality image alignment using information-theoretic approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
A novel framework for metric-based image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Prediction of brain MR scans in longitudinal tumor follow-up studies
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Local joint entropy based non-rigid multimodality image registration
Pattern Recognition Letters
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Nonrigid local image registration plays an important role in medical imaging. In this paper we focus on demon registration which is introduced by Thirion [1], and is comparable to fluid registration. Because demon registration cannot deal with multiple MRI modalities, we introduce a MRI modality transformation which changes the representation of a T1 scan into a T2 scan using the peaks in a joint histogram. We compare the performance between demon registration with modality transformation, demon registration with gradient images and Rueckerts [2] B-spline based free form deformation method in combination with mutual information. For this test we use perfectly aligned T1 and T2 slices from the BrainWeb database [3], which we local spherically distort. In conclusion demon registration with modality transformation gives the smallest registration errors, in case of local large spherical distortions and small bias fields.