Sandor S, Leahy R
TRW, Inc., Redondo Beach, CA 90278, USA.
IEEE Trans Med Imaging. 1997 Feb;16(1):41-54. doi: 10.1109/42.552054.
We describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach we take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge. Our deformable models are energy-minimizing elastic surfaces that can accurately locate image features. The models are parameterized with 3-D bicubic B-spline surfaces. We design the energy function such that cortical fissure (sulci) points on the model are attracted to fissure points on the image and the remaining model points are attracted to the brain surface. A conjugate gradient method minimizes the energy function, allowing the model to automatically converge to the smoothed brain surface. Finally, labels are propagated from the deformed atlas onto the high-resolution brain surface.
我们描述了一种在三维（3-D）磁共振（MR）脑图像中自动查找和标记皮质表面的计算机化方法。我们采用的方法是将预标记的脑图谱建模为一个物理对象，并赋予其弹性属性，使其能够自身变形贴合到预处理图像中的区域。预处理包括边界查找和形态学处理，该处理会自动从MR图像中提取大脑和脑沟，并提供大脑表面的平滑表示，可使可变形模型快速收敛于此。我们的可变形模型是能量最小化的弹性表面，能够准确地定位图像特征。这些模型由三维双三次B样条曲面参数化。我们设计能量函数，使得模型上的皮质沟（脑沟）点被吸引到图像上的沟点，而模型的其余点则被吸引到大脑表面。共轭梯度法使能量函数最小化，从而使模型自动收敛到平滑的大脑表面。最后，将标签从变形后的图谱传播到高分辨率的大脑表面。
