A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Exploiting the Self-Organizing Map for Medical Image Segmentation
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Noise Filtering Using Edge-Driven Adaptive Anisotropic Diffusion
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Nearest prototype classification of noisy data
Artificial Intelligence Review
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
Reusable components for partitioning clustering algorithms
Artificial Intelligence Review
D-Separation and computation of probability distributions in Bayesian networks
Artificial Intelligence Review
Review of brain MRI image segmentation methods
Artificial Intelligence Review
A new method for MR grayscale inhomogeneity correction
Artificial Intelligence Review
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Gaussian mixture model based segmentation methods for brain MRI images
Artificial Intelligence Review
Fuzzy C-mean based brain MRI segmentation algorithms
Artificial Intelligence Review
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Nowadays, fast scan techniques are used to reduce scanning times. These techniques raise scanning noise level in MRI systems. Instead of progress made in image de-noising, still, it is challenging. A novel edge-preserving neighbourhood filter for image enhancement is proposed. The main focus of this paper is to propose an adaptive filtering function to account for the image content while try to preserve edge of image. Proposed algorithm uses the edges of image to do edge-preserving neighbourhood filtering. Contribution of a sample, in neighbourhood of a pixel, in filtering, depends on the space between the pixel and the sample. In fact, the sample which there is edge between it and the pixel don't contribute in the grey level estimation. Promising experimental results on simulated and real brain images and comparison with state-of-art de-noising algorithm demonstrate the potential of proposed algorithm.