700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Segmentation of an image is the separation or division of the image into diverse regions of similar feature. In medical field, magnetic resonance image (MRI) is used to discriminate pathological tissues from normal tissues, particularly for brain tumors. Many methods are proposed and already in use for the tumor segmentation. In this paper, we have proposed a new sophisticated algorithm for the segmentation of the brain tumor. The image is segmented using swarm intelligence based approach by characterizing the ecological behaviour of clown fish. The algorithm is based on the queuing characteristics of the clown fish. In this paper a step by step methodology for the automatic MRI brain tumor segmentation is presented. Initially acquired MR brain images are divided into two approximately symmetric halves. Next skull is detected. Next an ellipse is fitted to the skull boundary, from which the line of symmetry is extracted. Final step is the tumor detection. The results of the proposed method are compared with well known other algorithms like Fuzzy C-Means clustering, self-organizing map and particle swarm optimization. The results show that the proposed algorithm is a promising method to segment the brain tumors accurately. Using Clown fish queuing and optimization algorithm, we achieved 100% sensitivity and 98% accuracy.