Computer
Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
The combination of edge detection and region extraction in nonparametric color image segmentation
Information Sciences: an International Journal
Information Sciences—Informatics and Computer Science: An International Journal
Multiscale Segmentation of Three-Dimensional MR Brain Images
International Journal of Computer Vision
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Compensation of Spatial Inhomogeneity in MRI Based on a Parametric Bias Estimate
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Automatic Robust Threshold Finding Aided by Fuzzy Information Granulation
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Information Sciences—Informatics and Computer Science: An International Journal
Introduction to Fuzzy Logic using MATLAB
Introduction to Fuzzy Logic using MATLAB
A high performance edge detector based on fuzzy inference rules
Information Sciences: an International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Fuzzy c-means approach to tissue classification in multimodal medical imaging
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Systemic approach to fuzzy logic formalization for approximate reasoning
Information Sciences: an International Journal
Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation
Engineering Applications of Artificial Intelligence
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Information Sciences: an International Journal
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Fuzzy logic-based generalized decision theory with imperfect information
Information Sciences: an International Journal
Fuzzy modeling incorporated with fuzzy d-s theory and fuzzy naive bayes
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Dempster Shafer neural network algorithm for land vehicle navigation application
Information Sciences: an International Journal
Information Sciences: an International Journal
Hi-index | 0.07 |
Brain Magnetic Resonance Imaging (MRI) segmentation is a challenging task due to the complex anatomical structure of brain tissues as well as intensity non-uniformity, partial volume effects and noise. Segmentation methods based on fuzzy approaches have been developed to overcome the uncertainty caused by these effects. In this study, a novel combination of fuzzy inference system and Dempster-Shafer Theory is applied to brain MRI for the purpose of segmentation where the pixel intensity and the spatial information are used as features. In the proposed modeling, the consequent part of rules is a Dempster-Shafer belief structure. The novelty aspect of this work is that the rules are paraphrased as evidences. The results show that the proposed algorithm, called FDSIS has satisfactory outputs on both simulated and real brain MRI datasets.