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Matching deformable atlas models to preprocessed magnetic resonance brain images

机译:将可变形的阿特拉斯模型与预处理的磁共振大脑图像匹配

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We describe a method for automatically labelling regions of three-dimensional (3-D) Magnetic Resonance (MR) scans of human brains. Labelling consists of attaching anatomic names to particular regions of the cortical surface that appear in these images. The approach we take is to automatically match a deformable anatomical atlas model to preprocessed brain images, where preprocessing consists of 3-D Marr-Hildreth edge detection and morphological operations. These filtering operations automatically extract the brain and sulci from an MR image and provide a smoothed representation of the brain surface to which the deformable model can rapidly converge. The model itself is a 3-D B-spline surface whose control vertices are chosen to minimize a cost function that reflects the distance of the model from boundary-like features in the image. Minimization takes place using a conjugate gradient technique.
机译:我们描述了一种自动标记三维(3-D)磁共振(MR)扫描人脑的区域的方法。标记包括将解剖名称附加到这些图像中出现的皮质表面的特定区域。我们采取的方法是自动将可变形的解剖结构模型与预处理的脑图像匹配,其中预处理包括3-D Marr-Hildreth边缘检测和形态操作。这些过滤操作从MR图像自动提取大脑和硫基,并提供可变形模型可以快速收敛的大脑表面的平滑表示。该模型本身是一个三维B样条表面,其选择控制顶点以最小化反映模型从图像中的边界特征的距离的成本函数。使用共轭梯度技术进行最小化。

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