An accurate skull stripping method based on simplex meshes and histogram analysis for magnetic resonance images

详细信息	查看全文 | 推荐本文 |

• 作者：Francisco J. Galdames^a ; ^d ; ^{fgaldame@ing.uchile.cl} ; ^{galdames.francisco@gmail.com} ; Fabrice Jaillet^c ; ^d ; ¹ ; ^{fjaillet@bat710.univ-lyon1.fr} ; Claudio A. Perez^a ; ^b ; ² ; ^{clperez@ing.uchile.cl}
• 关键词：Accurate skull stripping ; Non-brain tissue removal ; Brain surface extraction ; Brain surface simplex mesh modeling ; Patient specific mesh ; T1W MRI
• 刊名：Journal of Neuroscience Methods
• 出版年：2012
• 期刊代码：33_01650270
• 类别：neu
• 出版时间：15 May, 2012
• 卷：206
• 期：2
• 页码：103-119
• 文件大小：2554 K

摘要

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 (魏). Our method showed the best performance and differences were statistically significant (p < 0.05): J = 0.904 and 魏 = 0.950 on BrainWeb; J = 0.905 and 魏 = 0.950 on IBSR; J = 0.946 and 魏 = 0.972 on SVE.