Model-Based Interpretation of 3D Medical Images
Model-Based Interpretation of 3D Medical Images
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DOI：
10.5244/c.7.34
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
A. Hill;Ann Thornham;C. Taylor
中科院分区：
文献类型：
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作者：
A. Hill;Ann Thornham;C. Taylor
三维医学图像中解剖结构的自动分割和标记是一项具有实际意义的具有挑战性的任务。我们描述了一种基于模型的方法，它允许使用明确的解剖学知识进行稳健和准确的解释。我们的方法是基于点分布模型(PDMS)的3D扩展和相关的图像搜索算法。使用了全局遗传算法(GA)和局部主动形状模型(ASM)的组合搜索。我们已经建立了人脑的3D产品数据管理，描述了许多主要结构。利用该模型，我们对30幅不同个体的3D磁共振头部图像进行了自动判读。结果得到了定量的评估，支持了我们关于稳健和准确的解释的说法。
The automatic segmentation and labelling of anatomical structures in 3D medical images is a challenging task of practical importance. We describe a model-based approach which allows robust and accurate interpretation using explicit anatomical knowledge. Our method is based on the extension to 3D of Point Distribution Models (PDMs) and associated image search algorithms. A combination of global, Genetic Algorithm (GA), and local, Active Shape Model (ASM), search is used. We have built a 3D PDM of the human brain describing a number of major structures. Using this model we have obtained automatic interpretations for 30 3D Magnetic Resonance head images from different individuals. The results have been evaluated quantitatively and support our claim of robust and accurate interpretation.