New feature points based on geometric invariants for 3D image registration
International Journal of Computer Vision
A Framework for Uncertainty and Validation of 3-D RegistrationMethods Based on Points and Frames
International Journal of Computer Vision
Measuring Global and Local Spatial Correspondence Using Information Theory
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Analysis of Normal Brain Dissymmetry of Males and Females in MR Images
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Quantification of Changes in the Volume of Brain Structures
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Morphological Analysis of Brain Structures Using Spatial Normalization
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Measuring Lesion Growth from 3D Medical Images
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Analysis of Pulmonary Nodule Evolutions Using a Sequence of Three-Dimensional Thoracic CT Images
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Using SPM to Detect Evolving MS Lesions
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Change detection in diffusion MRI using multivariate statistical testing on tensors
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Non-rigid registration for colorectal cancer MR images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Quantification of growth and motion using non-rigid registration
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Exploiting HPC resources for the 3D-time series analysis of caries lesion activity
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
XSEDE-enabled high-throughput lesion activity assessment
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
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Physicians often perform diagnoses based on the evolution of lesions, tumors or anatomical structures through time. The objective of this paper is to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between successive temporal images. In studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this paper we apply this approach to the automatic detection and segmentation of multiple sclerosis lesions in time series of MRI images of the brain.