Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Graphical Models and Image Processing
Probabilistic Multiscale Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Knowledge-based segmentation and labeling of brain structures from MRI images
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
Pattern Recognition Letters
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images
Image and Vision Computing
Fuzzy pairwise Markov chain to segment correlated noisy data
Signal Processing
Joint brain parametric T1-map segmentation and RF inhomogeneity calibration
Journal of Biomedical Imaging
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Fuzzy information fusion scheme used to segment brain tumor from MR images
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Fast 3d brain segmentation using dual-front active contours with optional user-interaction
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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In this paper, we present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three main brain tissues in a brain dataset: gray matter, white matter, and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims at calculating a fuzzy membership in each voxel to indicate the partial volume degree, which is statistically modeled. Since our method is unsupervised, it first estimates the parameters of the fuzzy Markovian random field model using a stochastic gradient algorithm. The fuzzy Markovian segmentation is then performed automatically. The accuracy of the proposed method is quantitatively assessed on a digital phantom using an absolute average error and qualitatively tested on real MRI brain data. A comparison with the widely used fuzzy C-means algorithm is carried out to show numerous advantages of our method.