Abstract: The MRI-based quantity analysis of Substantia Nigra（SN） in human brain has more and more value in diagnosis of Parkinson disease in today.An anatomic knowledge-constrained algorithm is described based on active surface model and adaptive region growth to automatically delineate the SN region from a magnetic resonance image.The result of the algorithm can be used to calculate position，shape and volume and help early clinical diagnosis as well as treating effect of SN.Experimental results show that the algorithm has good accuracy and adaptation.

Key words: 3D segmentation, anatomic knowledge-constrain, substantia nigra, Parkinson disease, active model, adaptive region growth

摘要： 随着MRI技术的发展，人脑黑质核团的MRI定量分析在帕金森病诊断中的应用价值越来越高。提出一种解剖先验知识为约束基于动态曲面模型和自适应区域增长的自动3D分割方法，来完成黑质形状结构的精确三维分割和提取。由此获得黑质的位置、形状和体积，以期辅助临床上对早期帕金森病的诊断和评价治疗效果。分割实验表明该方法精确性高，具有较强的自适应性。

关键词: 三维分割, 解剖知识约束, 黑质, 帕金森病, 动态模型, 自适应区域增长