Brain extraction from cerebral MRI volume using a hybrid level set based active contour neighborhood model

Shaofeng Jiang, Weirui Zhang, Yu Wang, Zhen Chen

BACKGROUND: The extraction of brain tissue from cerebral MRI volume is an important pre-procedure for neuroimage analyses. The authors have developed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model. METHODS: The method uses a nonlinear speed function in the hybrid level set model to eliminate boundary leakage. When using the new hybrid level set model an active contour neighborhood model is applied iteratively in the neighborhood of brain boundary. A slice by slice contour initial method is proposed to obtain the neighborhood of the brain boundary. The method was applied to the internet brain MRI data provided by the Internet Brain Segmentation Repository (IBSR). RESULTS: In testing, a mean Dice similarity coefficient of 0.95+/-0.02 and a mean Hausdorff distance of 12.4+/-4.5 were obtained when performing our method across the IBSR data set (18 x1.5 mm scans). The results obtained using our method were very similar to those produced using manual segmentation and achieved the smallest mean Hausdorff distance on the IBSR data. CONCLUSIONS: An automatic method of brain extraction from cerebral MRI volume was achieved and produced competitively accurate results.