Biomedical Engineering Laboratory, Department of Electrical Engineering, Universidad de Chile, Santiago, Chile.
J Neurosci Methods. 2012;206(2):103-19. doi: 10.1016/j.jneumeth.2012.02.017. Epub 2012 Feb 23.
Skull stripping methods are designed to eliminate the non-brain tissue in magnetic resonance (MR) brain images. Removal of non-brain tissues is a fundamental step in enabling the processing of brain MR images. The aim of this study is to develop an automatic accurate skull stripping method based on deformable models and histogram analysis. A rough-segmentation step is used to find the optimal starting point for the deformation and is based on thresholds and morphological operators. Thresholds are computed using comparisons with an atlas, and modeling by Gaussians. The deformable model is based on a simplex mesh and its deformation is controlled by the image local gray levels and the information obtained on the gray level modeling of the rough-segmentation. Our Simplex Mesh and Histogram Analysis Skull Stripping (SMHASS) method was tested on the following international databases commonly used in scientific articles: BrainWeb, Internet Brain Segmentation Repository (IBSR), and Segmentation Validation Engine (SVE). A comparison was performed against three of the best skull stripping methods previously published: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), and Hybrid Watershed Algorithm (HWA). Performance was measured using the Jaccard index (J) and Dice coefficient (κ). Our method showed the best performance and differences were statistically significant (p<0.05): J=0.904 and κ=0.950 on BrainWeb; J=0.905 and κ=0.950 on IBSR; J=0.946 and κ=0.972 on SVE.
颅骨剥离方法旨在消除磁共振（MR）脑图像中的非脑组织。去除非脑组织是处理脑 MR 图像的基本步骤。本研究旨在开发一种基于变形模型和直方图分析的自动准确颅骨剥离方法。粗分割步骤用于找到变形的最佳起始点，基于阈值和形态算子。阈值通过与图谱的比较和高斯建模来计算。变形模型基于单形网格，其变形由图像局部灰度级和粗分割的灰度级建模获得的信息控制。我们的 Simplex Mesh 和 Histogram Analysis Skull Stripping (SMHASS) 方法在以下国际数据库中进行了测试，这些数据库常用于科学文章：BrainWeb、Internet Brain Segmentation Repository (IBSR) 和 Segmentation Validation Engine (SVE)。与之前发表的三种最佳颅骨剥离方法进行了比较：Brain Extraction Tool (BET)、Brain Surface Extractor (BSE) 和 Hybrid Watershed Algorithm (HWA)。使用 Jaccard 指数 (J) 和 Dice 系数 (κ) 来衡量性能。我们的方法表现最佳，差异具有统计学意义（p<0.05）：在 BrainWeb 上 J=0.904 和 κ=0.950；在 IBSR 上 J=0.905 和 κ=0.950；在 SVE 上 J=0.946 和 κ=0.972。
