Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error.
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.
Lee
Jong-Min
JM
Department of Biomedical Engineering, College of Medicine, Hanyang University, Haengdang-dong, Seongdong-ku, 133-791 Seoul, South Korea.
Yoon
Uicheul
U
Nam
Sang-Hee
SH
Kim
Jung-Hyun
JH
Kim
In-Young
IY
Kim
Sun I
SI
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results