Glioma Segmentation from Multimodal MRI Images Based on Superpixel Clustering

Glioma is the most common and aggressive brain tumor leading to a very short life expectancy in their highest grade. Accurate segmentation of multiple gliomas from multimodal MRI is a prerequisite for medical diagnosis and contributes to improving the quality of life of oncological patients. In order to effectively extract the characteristics of glioma and improve the segmentation accuracy, we propose a hierarchical segmentation algorithm based on superpixel sclustering for HGGs. The MRI image is enhanced and denoised with preprocessing methods. Then the MRI image is segmented with superpixel approaches. The dynamic region merging algorithm is performed to progressively integrate the neighbouring superpixels, from which the tumor region is extracted with random forest. Finally, three kinds of lesion regions, necrosis, oedema, and enhancing tissues are detected with the K-means clustering method. Experimental results show that the proposed algorithm has an improvement in segmentation accuracy.