Brain tumor segmentation from medical images is a clinical requirement for brain tumor diagnosis and radiotherapy planning.However

automatic or semi-automatic segmentation of the brain tumor is still a challenging task due to the high diversities and the ambiguous boundaries in the appearance of tumor tissue.To solve this problem

we propose a brain tumor segmentation method based on softmax regression and graph model.Firstly

the training samples are labeled from the multi-modality magnetic resonance images(MRI).Then

the softmax regression method is used to train the samples to obtain the parameters of this regression model and calculate the probabilities of each pixel belonging to different labels.At last

the probabilities calculated in the previous step are introduced to a graph-cut based model.This model is minimized with a min-cut/max-flow method to obtain the final tumor segmentation results.The experiment results demonstrate superior performance in brain tumor segmentation.