Li Yue, Mandal Mrinal, Ahmed S Nizam
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5111-4. doi: 10.1109/EMBC.2013.6610698.
In the diagnosis of various brain disorders by analyzing the brain magnetic resonance images (MRI), the segmentation of corpus callosum (CC) is a crucial step. In this paper, we propose a fully automated technique for CC segmentation in the T1-weighted midsagittal brain MRIs. An adaptive mean shift clustering technique is first used to cluster homogenous regions in the image. In order to distinguish the CC from other brain tissues, area analysis, template matching, in conjunction with the shape and location analysis are proposed to identify the CC area. The boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) model, and evolved to get the final segmentation result. Experimental results demonstrate that the proposed technique overcomes the problem of manual initialization in existing GAC technique, and provides a reliable segmentation performance.
在通过分析脑磁共振成像（MRI）诊断各种脑部疾病时，胼胝体（CC）的分割是关键步骤。本文提出了一种用于在T1加权矢状位脑MRI中对CC进行全自动分割的技术。首先使用自适应均值漂移聚类技术对图像中的同质区域进行聚类。为了将CC与其他脑组织区分开来，提出了面积分析、模板匹配，并结合形状和位置分析来识别CC区域。然后将检测到的CC区域的边界用作几何活动轮廓（GAC）模型中的初始轮廓，并进行演化以获得最终的分割结果。实验结果表明，所提出的技术克服了现有GAC技术中手动初始化的问题，并提供了可靠的分割性能。
