Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error

Comput Biol Med. 2003 Nov;33(6):495-507. doi: 10.1016/s0010-4825(03)00022-2.

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology*
  • Humans
  • Image Processing, Computer-Assisted*
  • Magnetic Resonance Imaging / methods*
  • Reproducibility of Results