Segmentation of GBM in MRI images using an efficient speed function based on level set method

Accurate segmentation and characterization of abnormalities in brain tumor are challenging task, especially in the case of GBM tumors, where the ambiguities presented in the boundaries of these tumors necessitates using efficient segmentation method. Level set methods have proven to be a flexible and powerful tool for image segmentation because of being shape-driven method with a properly defined speed function to grow or shrink the boundaries to segment complex objects of interest, precisely. In this study a combined level set algorithm consists of both region and boundary terms for GBM segmentation is proposed. The modified speed function incorporates threshold based level set and the Laplacian filter to highlight the fine details for performing an accurate extraction of the tumor region using multiple seed points selected by the user. An evaluation was performed on a dataset containing 6 patients with GBM by using three measures Dice, false positive error (FPE) and false negative error (FNE). Manual segmentation of GBM is considered as gold standard. Compared to traditional method, the mean of FPE and FNE are improved by 53.5% and 53.1%, respectively. The mean of Dice coefficients between our results and gold standard measurement reached to 0.88. As the results proved, the proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function. Our method is also robust to change of parameters.