Image fusion for following-up brain tumor evolution
Su Ruan, Nan Zhang, Qingmin Liao, Yuemin Zhu · 2011
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination per four months): feature selection, SVM-based segmentation, and contour refinement. The training of the tumor is carried out only on the first MRI examination (before the treatment). Six feature selection methods are tested in our system. The quantitative comparisons of the methods' and the expert's manual traces demonstrate the effectiveness of the fusion by the feature selection. Results of following-up three patients over one year show that our system can provide a good tool to evaluate the therapeutic treatment.