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