Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error - Lee, Jong-Min; Yoon, Uicheul; Nam, Sang Hee; Kim, Jung-Hyun; Kim, In-Young; Kim, Sun I. (Computers in biology and medicine, 2003)
The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.