An Improved BET Method for Brain Segmentation
Liping Wang, Ziming Zeng, Reyer Zwiggelaar · 2014
The Brain Extraction Tool (BET) developed by Smith is widely used for brain segmentation due to its simplicity, accuracy and insensitivity to parameter settings. However, it typically requires a large number of iterations to generate acceptable results. It also sometimes fails to recognize boundaries of the brain. Moreover, obvious under-segmentation occurs for some datasets. In this paper, we present an improved BET method where at each iteration, we enhance the vertex displacement, add a new search path and embed an independent surface reconstruction process. These strategies lead to much faster convergence. Furthermore, a scheme based on fuzzy c-means is proposed to refine the segmentation. Experimental results based on various datsets demonstrated that the proposed method significantly outperforms the original BET and other competing methods.