An Automatic Brain Tumor Extraction System Using Different Segmentation Methods

This paper present algorithms for brain tumor extraction from Magnetic Resonance Image (MRI) using four different methods namely Otsu, K-means, Fuzzy-c-Means and thresholding. Brain tumor is inherently serious and life-threatening disease which can threat life of a human being. A robust automated brain tumor detection system with high accuracy is always preferable over the manual detection. The main objective of this paper is to develop a fully automated brain tumor detection system that can detect and extract tumor from MR Image of brain. In this paper a sophisticated automated tumor detection system is presented having very good accuracy with low computational time. This paper also gives the comparison between the algorithms presented. Experimental results validate that all the algorithms presented in this paper have accuracy more than 87.32% of detecting the brain tumor. A dataset of 25 MR Images of brain have been used for testing the system and recording the experimental results. It has been found that algorithm developed using Fuzzy c-means algorithm gives the highest accuracy i.e. 90.57% while thresholding algorithm takes the least time to compute the results i.e. 1.11 seconds.