A Framework of Automatic Brain Tumor Segmentation Method Based on Information Fusion of Structural and Functional MRI Signals

The brain tumor segmentation method of MRI images is of key importance for clinical analysis of glioma. The majority of existing methods are focused on structural MRI such as T1-weighted and T2-weighted. Additionally, functional MRI including Magnetic Resonance Spectroscopy (MRS), Diffusion Weighted Imaging (DWI), and Blood-Oxygen-Level Dependent (BOLD) can also contribute to increasing the validity and accuracy of the results. This paper proposes a framework of automatic brain tumor segmentation method based on information fusion of structural and functional signals. The method consists of four steps: intensity mapping for feature, region growing for tumor, region growing for edema and necrosis detection. The performance evaluation has been done by using some clinical MRI data with glioma. Comparing the segmentation results with the manual segmentation as "ground truth", it has achieved average Dice score 83.7% in the tumor, and 88.5% in the whole lesion area, which indicated the validity and robustness of the proposed method.