Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Understanding Intensity Non-uniformity in MRI
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
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
A parametric gradient descent MRI intensity inhomogeneity correction algorithm
Pattern Recognition Letters
Nonlocal Variational Image Deblurring Models in the Presence of Gaussian or Impulse Noise
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A Non-Local Fuzzy Segmentation Method: Application to Brain MRI
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A non-local fuzzy segmentation method: Application to brain MRI
Pattern Recognition
Local gaussian distribution fitting based FCM algorithm for brain MR image segmentation
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Image segmentation plays an important role in medical image analysis. The most widely used image segmentation algorithms, region-based methods that typically rely on the homogeneity of image intensities in the regions of interest, often fail to provide accurate segmentation results due to the existence of bias field, heavy noise and rich structures. In this paper, we incorporate nonlocal regularization mechanism in the coherent local intensity clustering formulation for brain image segmentation with simultaneously estimating bias field and denoising, specially preserving good structures. We define an energy functional with a local data fitting term, two nonlocal regularization terms for both image and membership functions, and a $$L_2$$L2 image fidelity term. By minimizing the energy, we get good segmentation results with well preserved structures. Meanwhile, the bias estimation and noise reduction can also be achieved. Experiments performed on synthetic and clinical brain magnetic resonance imaging data and comparisons with other methods are given to demonstrate that by introducing the nonlocal regularization mechanism, we can get more regularized segmentation results.