Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Road, Daan, 10617, Taipei, Taiwan.
Med Biol Eng Comput. 2013 Oct;51(10):1091-104. doi: 10.1007/s11517-013-1089-7. Epub 2013 Jun 7.
Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.
颅骨剥离磁共振（MR）图像是医学图像分析中最重要的预处理步骤之一。我们提出了一种基于自适应气球蛇（ABS）模型的混合颅骨剥离算法。所提出的框架由两个阶段组成：首先，模糊可能性 C 均值（FPCM）用于像素聚类，这提供了一个与干净清晰的大脑边界相关的标记图像。在第二阶段，基于 FPCM 结果在大脑表面之外初始化轮廓，并在自适应气球蛇模型的指导下演化。该模型旨在驱动轮廓向内部法向以捕获大脑边界。整个体积通过从中心切片逐片向两端切片进行分割。我们的 ABS 算法应用于许多脑磁共振图像数据集，并与几种最先进的方法进行了比较。使用了四个相似性度量来评估所提出技术的性能。实验结果表明，我们的方法产生了准确的分割结果，具有更高的一致性得分。ABS 算法的有效性使其成为各种颅骨剥离应用和研究中很有前途和潜在的工具。
