Hinshaw K P, Brinkley J F
Dept. of Computer Science and Engineering, University of Washington, Seattle 98195, USA.
Proc AMIA Annu Fall Symp. 1997:469-73.
Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters.
医学图像的精确分割是计算机视觉中的主要挑战之一。仅依赖强度信息的方法常常失败，因为多个结构中会出现相似的强度值。本文提出一种利用形状知识来指导分割过程的方法，并将其应用于寻找脑表面的任务中。一个包含局部形状约束的三维模型被拟合到磁共振体数据集上。由此产生的低分辨率表面用于屏蔽远离皮质表面的区域，使等值面提取算法能够分离出更详细的表面边界。该技术生成的表面与其他方法所获得的表面相当，且无需用户调整大量临时参数。
