Abstract: Image segmentation refers to the process of partitioning an image into mutually exclusive regions. Automatic brain tumor segmentation has become a key component for the future of brain tumor treatment.Gliomas are the most common primary brain tumors, evolving from the cerebral supportive cells. Despite intensive research, automatic segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture and other factors. There are many techniques have been proposed in the literature for automatic brain tumor segmentation, those approaches arise from the supervised learning standpoint which requires a labelled training dataset from which to infer the models of the classes whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumor segmentation based on Magnetic Resonance (MR) images. This study presents a novel unsupervised image clustering based on Harmonic Search Optimization (HSO) and Fuzzy C-Means (FCM) for Glioblastoma Multiform (GBM) Tumor segmentation. The performance of FCM algorithm to obtain an optimal solution depends on the initial positions of the centroids of the clusters. In the existing FCM, the centroids are initialized randomly. This leads to increase in time to reach the optimal solution. In order to accelerate the segmentation process a new method called HSO based FCM is proposed. The proposed algorithms exploits an initial step derived from the HSO, considering Otsu Method as the objective function. After finding the cluster centers using HSO, FCM algorithm is initialized with these cluster centers. Finally, active contours are used for GBM tumor segmentation and boundary tracking. The experimental results confirms that the proposed method as a viable alternative for GBM tumor segmentation.