Efficient implementation for classifying and segmenting of computed tomography brain tumour images using modified region growing with lion algorithm Online publication date: Sat, 04-Mar-2017
by Thiagarajan Ramakrishnan; Balasubramanian Sankaragomathi
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 23, No. 2/3/4, 2017
Abstract: The study of Computed Tomography (CT) images considered in image segmentation is very important as it plays a vital part in identifying the different kinds of tumour. The classification of the tumour and the non-tumour images is followed by the segmentation of tumour region in CT images which are done by the proposed methodology. The process of classifying is done by Support Vector Machine (SVM) with linear kernel as well as Sequential Minimal Optimisation (SMO). After the classification process, segmentation is performed by the Modified Region Growing (MRG) with a threshold optimisation using Lion Algorithm (LA). In comparison, the sensitivity value of the proposed approach is appreciably higher as 91.52% than that of the existing method of MRG-Grey Wolf Optimisation (MRG-GWO). The comparative analysis in terms of sensitivity, specificity and accuracy is done for the proposed as well as for the existing techniques.
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