ABSTRACT
Purpose: Glioblastoma Multiforme [GBM] brain tumor is heterogeneous in nature; so, its quantification depends on how to accurately segment different parts of the tumor, i.e. active tumor, edema and necrosis. This procedure becomes more effective when physiological information like diffusion-weighted-imaging [DWI] and perfusion-weighted-imaging [PWI] are incorporated with the anatomical MRI. In this preliminary tumor quantification work, the idea is to characterize different regions of the GBM tumors in an MRI-based multi-parametric approach to achieve more accurate characterization of pathological regions, which cannot be obtained by using individual modalities. Methods: For this purpose, three MR sequences, namely T2-weighted imaging [anatomical MR imaging], PWI and DWI of five GBM patients were acquired. To enhance the delineation of the boundaries of each pathological region [peri-tumoral edema, tumor and necrosis], the spatial fuzzy C-means [FCM] algorithm is combined with the region growing [RG] method. Results: The results show that exploiting the multi-parametric approach along with the proposed segmentation method can improve characterization of tumor cells, edema and necrosis in comparison to mono-parametric imaging approach. Conclusion: The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the treatment planning, e.g. in radiotherapy