Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering

Metastasis brain tumor develops multiple tumors at asymmetrical location of the human brain. MRI imaging is one of the prudent mechanisms to extract the tumor regions and to map the brain for diagnosing. For the better diagnosis, one must detect the tumor accurately and need to calculate the area and volume of the tumor exactly. In this paper, we proposed a novel resolution enhancement technique to improve the quality of MR brain image and Optimized Hybrid Mean Shift Clustering (OHMSC) with region split and merge algorithm to detect the tumor cells from the original MR images and to estimate the tumors from different locations. Simulation results show that the proposed algorithm has performed superior to conventional clustering algorithms such as Fuzzy C-Means (FCM), K-Means and even optimized pillar algorithm.