An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
Pattern Recognition Letters
Joint LMMSE Estimation of DWI Data for DTI Processing
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Combined Wavelet and Nonlinear Filtering for MRI Phase Images
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Noise-driven anisotropic diffusion filtering of MRI
IEEE Transactions on Image Processing
An Object-Based Method for Rician Noise Estimation in MR Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Nonparametric statistical tests for exploration of correlation and nonstationarity in images
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Review of brain MRI image segmentation methods
Artificial Intelligence Review
Maximum likelihood estimators in magnetic resonance imaging
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Multicomponent MR image denoising
Journal of Biomedical Imaging
Collateral filtering of magnetic resonance images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Wavelet-based statistical analysis in functional neuroimaging
WAMUS'06 Proceedings of the 6th WSEAS international conference on Wavelet analysis & multirate systems
A general system for automatic biomedical image segmentation using intensity neighborhoods
Journal of Biomedical Imaging
Wavelet-based de-noising techniques in MRI
Computer Methods and Programs in Biomedicine
De-noising method in the wavelet packets domain for phase images
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Nonparametric neighborhood statistics for MRI denoising
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Efficient and robust nonlocal means denoising of MR data based on salient features matching
Computer Methods and Programs in Biomedicine
Wavelet-Based methods for improving signal-to-noise ratio in phase images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
A novel approach for adaptive unsupervised segmentation of MRI brain images
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
An integrated algorithm for MRI brain images segmentation
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Thermal noise estimation and removal in MRI: a noise cancellation approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Wavelet based speech presence probability estimator for speech enhancement
Digital Signal Processing
Groupwise segmentation improves neuroimaging classification accuracy
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
DWI denoising using spatial, angular, and radiometric filtering
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
Medical image denoising using adaptive fusion of curvelet transform and total variation
Computers and Electrical Engineering
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
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It is well known that magnetic resonance magnitude image data obey a Rician distribution. Unlike additive Gaussian noise, Rician “noise” is signal-dependent, and separating signal from noise is a difficult task. Rician noise is especially problematic in low signal-to-noise ratio (SNR) regimes where it not only causes random fluctuations, but also introduces a signal-dependent bias to the data that reduces image contrast. This paper studies wavelet-domain filtering methods for Rician noise removal. We present a novel wavelet-domain filter that adapts to variations in both the signal and the noise