Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification…

• NN
  National Natural Science Foundation of China

  Awards: 81101109, 31371009, 2012BAI14B02, 863 Program
• NH
  National High-tech Research and Development Program