Fuzzy set theoretic measure for automatic feature evaluation
IEEE Transactions on Systems, Man and Cybernetics
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Fuzzy rule base generation for classification and its minimization via modified threshold accepting
Fuzzy Sets and Systems - Special issue on clustering and learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
A Fuzzy Classifier System Using the Pittsburgh Approach
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Optimizing the Fuzzy Classification System through Genetic Algorithm
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
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Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable.