Abstract: Partial volume effect and uneven gray level of magnetic resonance (MR) imaging make the brain tumor image segmentation low accuracy. In order to solve the problem, a new brain tumor image segmentation method is proposed, which is a rough set adaptive granularity method. The method firstly uses the tumor features in the image to adaptively select the optimal granularity. It uses the rough set idea to simulate the upper and lower approximation of the target and background regions. The best threshold for MR brain tumor image segmentation is obtained  by optimizing the roughness of the target and background regions. This method can extract the area of brain tumors. The experimental results show that the method is superior to the traditional rough set segmentation methods. This method has certain practicability and flexibility.

Key words: magnetic resonance imaging, brain tumor, image segmentation, rough sets