An automated brain tumor segmentation framework using multimodal MRI

Abstract

An automated region of interest (ROI) segmentation framework is proposed for edema detection and brain tumor segmentation from brain magnetic resonance images (MRI). In order to further improve the accuracy of the framework, multimodal MRI data are applied in this framework. The framework mainly contains three stages. First the cluster algorithm and morphological operation are used for detecting the abnormal tissue i.e. edema so as to automatically initialize the level set method. Then edge-based level set method combining regional information is used for edema segmentation from Fluid Attenuated Inversion Recovery (FLAIR) MRI. The final segmentation result of brain tumor is obtained by using the cluster method, filling algorithm and opening (morphology) operation at T1 contrast-enhanced (T1c) MRI. The experiments are carried out on two modalities MRI slices of 8 true patients, which have the matching ground truth of the edema and tumor. Experimental results demonstrate the effectiveness of our algorithm.