Multisubject Non-rigid Registration of Brain MRI Using Intensity and Geometric Features
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Connectivity-based parcellation of the cortical mantle using q-ball diffusion imaging
Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
Bias artifact suppression on MR volumes
Computer Methods and Programs in Biomedicine
A new method for MR grayscale inhomogeneity correction
Artificial Intelligence Review
Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Non-uniformity correction using cosine functions basis and total variation constraint
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Retrospective illumination correction of retinal images
Journal of Biomedical Imaging
IPCAI'11 Proceedings of the Second international conference on Information processing in computer-assisted interventions
Joint restoration of bi-contrast MRI data for spatial intensity non-uniformities
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Highly accurate segmentation of brain tissue and subcortical gray matter from newborn MRI
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Nonparametric neighborhood statistics for MRI denoising
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Spatial intensity correction of fluorescent confocal laser scanning microscope images
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
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This paper outlines a fully automatic method for the correction of intensity nonuniformity in MR images. This method does not require any a priori model of the tissue classes. The basic idea is that entropy is a good measure of the image quality, which can be minimized in order to overcome the bias problem. Therefore, the optimal correcting field is defined by the minimum of a functional combining the restored image entropy and a measure of the field smoothness. This measure stems from the usual physical analogy with membranes. A third term added to the functional prevents the optimal field from being uniformly null. The functional is minimized using a fast annealing schedule. The performance of the method is evaluated using both real and simulated data.