Superpixel-Based Segmentation of Glioblastoma Multiforme from Multimodal MR Images

Due to complex imaging characteristics such as large diversity in shapes and appearances combining with deformation of surrounding tissues, it is a challenging task to segment glioblastoma multiforme (GBM) from multimodal MR images. In particular, it is important to capture the heterogeneous features of enhanced tumor, necrosis, and non-enhancing T2 hyperintense regions (T2HI) to determine the aggressiveness of the tumor from neuroimaging. In this paper, we propose a superpixel-based graph spectral clustering method to improve the robustness of GBM segmentation. A new graph spectral clustering algorithm is designed to group superpixels to different tissue types. First, a local k-means clustering with weighted distances is employed to segment the MR images into a number of homogeneous regions, called superpixels. Then, the spectral clustering algorithm is utilized to extract the enhanced tumor, necrosis, and T2HI by considering the superpixel map as a graph. Experiment results demonstrate better performance of the proposed method by comparing with pixel-based and the normalized cut segmentation methods.