Structure optimization of fuzzy neural network by genetic algorithm
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Zone Content Classification and its Performance Evaluation
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
Pattern Recognition Letters
Pattern Recognition Letters
IEEE Transactions on Information Technology in Biomedicine
A methodology for constructing fuzzy algorithms for learning vector quantization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Detecting pathologies with homology algorithms in magnetic resonance images of brain
Machine Graphics & Vision International Journal
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Computer Vision and Image Understanding
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Subspace clustering of high-dimensional data: an evolutionary approach
Applied Computational Intelligence and Soft Computing
Hi-index | 12.06 |
Magnetic resonance imaging (MRI) segmentation has been implemented by many clustering techniques, such as k-means, fuzzy c-means (FCM), learning-vector quantization (LVQ) and fuzzy algorithms for LVQ (FALVQ). Although these algorithms have been successful in applying MRI segmentation, obtaining the right number of clusters and adapting to different cluster characteristics are still not satisfactorily addressed. This report proposes an optimization technique, a hierarchical genetic algorithm with a fuzzy learning-vector quantization network (HGALVQ), to segment multi-spectral human-brain MRI. Evaluation of this approach is based on a real case with human-brain MRI of an individual suffering from meningioma. The HGALVQ is verified by the comparison with other popular clustering algorithms such as k-means, FCM, FALVQ, LVQ, and simulated annealing. Experimental results show that HGALVQ not only returns an appropriate number of clusters and also outperforms other methods in specificity.