A novel segmentation method for isointense MRI brain tumor

This paper presents a novel segmentation method for isointense signal tumor appeared in T1-weighted or T2-weighted magnetic resonance (MR) images. The proposed method improves the well-known Grow-cut algorithm using the improved local transition rule. It applied the level set theory to extract tumor from the background by using strength probability surface map by threshold value. Heaviside step function are applied to assign the boundary among seed and background. For performance evaluation, tumor datasets on isointense signal with T1-weighted MRI acquired from Kitware/MIDAS repository are experimented throughout. The well-known grow-cut and tumorcut algorithms are compared using dice similarity coefficient (DSC). In this regard, the proposed method provides the better results by reporting DSC of 84.17 % higher than Grow-cut and Tumorcut with 80.81% and 80.14%, respectively.