Magnetic Resonance Imaging (MRI) technique is one of the most useful diagnostic tools for human soft tissue analysis. Moreover, the brain anatomy features and internal tissue architecture of brain tumor are a complex task in case of 3-D anatomy. The additional spatial relationship in transverse, longitudinal planes and the coronal plane information has been proved to be helpful for clinical applications. This study extends the computation of gray level co-occurrence matrix (GLCM) and Run length matrix (RLM) to a three-dimensional form for feature extraction. The sub-selection of rich optimal bank of features to model a classifier is achieved with custom Genetic Algorithm design. An improved Extreme Learning Machine (ELM) classifier algorithm is explored, for training single hidden layer artificial neural network, integrating an enhanced swarm-based method in optimization of the best parameters (input-weights, bias, norm and hidden neurons), enhancing generalization and conditioning of the algorithm. The method is modeled for automatic brain tissue and pathological tumor classification and segmentation of 3D MRI tumor images. The method proposed demonstrates good generalization capability from the best individuals obtained in the learning phase to handle sparse image data on publically available benchmark dataset and real time data sets.
"Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images Using Improved PSO for Extreme Learning Machine," Progress In Electromagnetics Research B,
Vol. 49, 31-54, 2013. doi:10.2528/PIERB13010202
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