Brain Tumor Segmentation through Data Fusion of T2-Weighted Image and MR Spectroscopy

Brain tumor segmentation is an important technique in computer aided diagnosis. To improve this, it is necessary to use biochemical information provided by magnetic resonance spectroscopy (MRS). An important issue is how to combine the multimodal signals, such as MRS and structure images, and how to use the combined information to make a decision. A data fusion method is proposed in this paper to perform an automatic segmentation of brain tumor. The combinational data of MRS and T₂-weighted image should be enhanced by an operation of exponential companding. It consists of five steps: multi-voxel MRS (or CSI) data processing, localization and VOI extraction, data combination, exponential companding, and region growing. Two glioma patients\´ data provided by Tiantan hospital of China have been used to evaluate our method. Two "ground truth", tumor with edema and tumor only, used for results comparison are manual labels made by neuro radiologists of Tiantan and Nanfang hospitals of China. The segmentation result represents MRS-weighted T₂ structure image in tumor region. Its performance is 99% correct detection for tumor only and 98% for tumor with potential edema, and the false detection are 9% and 6% inside VOI, respectively. The proposed method is also a simple information fusion strategy.