In recent years, clustering has become well known for various researchers due to various application fields like communication, wireless networking, and biomedical domain and so on. So, much different research has already been made by the researchers to develop an improved algorithm for clustering. An optimization is one of the well-known processes that has been effectively utilized for clustering. In this paper, Invasive weed optimization (IWO) based centroid initialization for fuzzy c-means clustering (FCM) in medical image segmentation (KFCM-IWO-MIS) is proposed. For MRI brain tissue segmentation, KFCM is most preferable technique because of its performance accuracy. The major limitation of the conventional KFCM is random centroids initialization, because it leads to raising the execution time to reach the best resolution. In order to accelerate the segmentation process, IWO is used to adjust the centroids of required clusters. The quantitative measures of results were compared using the metrics are number of iterations and processing time. The number of iterations and processing of KFCM-IWO-MIS method take minimum value while compared to conventional KFCM. The KFCM-IWO-MIS method is very efficient and faster than conventional KFCM for brain tissue segmentation.

clustering, centroid initialization, Invasive weed optimization(IWO), Kernel fuzzy C-means (KFCM), MRI brain tissue segmentation

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