Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
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 regularization strategy for image deconvolution
Pattern Recognition Letters
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
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
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
A Spatio-temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Fuzzy c-means clustering with non local spatial information for noisy image segmentation
Frontiers of Computer Science in China
Data-driven cortex segmentation in reconstructed fetal MRI by using structural constraints
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Image segmentation on GPGPUs: a cellular automata-based approach
Proceedings of the 2013 Summer Computer Simulation Conference
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
Computer Methods and Programs in Biomedicine
Machine Vision and Applications
Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images
International Journal of Hybrid Intelligent Systems
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The Fuzzy C-Means (FCM) algorithm is a widely used and flexible approach to automated image segmentation, especially in the field of brain tissue segmentation from 3D MRI, where it addresses the problem of partial volume effects. In order to improve its robustness to classical image deterioration, namely noise and bias field artifacts, which arise in the MRI acquisition process, we propose to integrate into the FCM segmentation methodology concepts inspired by the non-local (NL) framework, initially defined and considered in the context of image restoration. The key algorithmic contributions of this article are the definition of an NL data term and an NL regularisation term to efficiently handle intensity inhomogeneities and noise in the data. The resulting new energy formulation is then built into an NL-FCM brain tissue segmentation algorithm. Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms.