A Novel Two-Stage Approach For Automatic Detection of Brain Tumor
Keywords:Brain tumor detection, Hierarchical Centroid Shape Descriptor, Modified K-means, MRI, Dice index.
AbstractBrain tumor is one of the most life-threatening diseases, and it is the most common type of cancer that occurs among those in the age group belonging to 0-19. It is also a major cause of cancer-related deaths in children (males and females) under age 20 hence its detection should be fast and accurate. Manual detection of brain tumors using MRI scan images is effective but time-consuming. Many automation techniques and algorithms for detection of brain tumors are being proposed recently. In this paper, we propose an integrated two-step approach combining modified K-means clustering algorithm and Hierarchical Centroid Shape Descriptor (HCSD). The images are clustered using modified K-means based on pixel intensity, and then HCSD helps to select those having a specific shape thus making this approach more effective and reliable. Simulation of the proposed work is done in MATLAB R2013a. Tests are carried out on T1 weighted MRI scan images.
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