Segregation of MRI brain image using hybrid evolutionary clustering algorithm
by N. Rajalakshmi; V. Lakshmi Prabha
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 18, No. 1, 2015

Abstract: The present research is on computer-aided classification of the magnetic resonance brain images. The method proposed congregates on colour-converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on Information Gain and Sequential Forward Floating Search (IGSFFS) and Multi-Class Support Vector Machine (MC-SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The present research acknowledges the colour-converted segmentation by new hybrid evolutionary clustering algorithm which is the mixture of weighted firefly and K-means algorithm to overcome local optima problems in firefly algorithm. Further random cluster initialisation is also modelled. The results of the simulation show that the performance of the proposed algorithm has better segmentation accuracy than the other algorithms such as colour-converted PSO-K-means and K-means clustering algorithm. The performance of the method is evaluated using classification accuracy, sensitivity, specificity and Receiver Operating Characteristic (ROC) curves. The results show that the highest classification accuracy of greater than 98% is obtained for the proposed diagnostic model, and this is very promising compared to the previously reported results.

Online publication date: Sun, 14-Jun-2015

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