Dept. of Electr. Eng., Yale Univ., New Haven, CT.
IEEE Trans Med Imaging. 1996;15(6):859-70. doi: 10.1109/42.544503.
Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.
准确地分割和量化结构是生物医学图像分析中的一个关键问题。图像分割的两种传统方法，基于区域的分割和边界发现，往往受到各种限制。在这里，作者提出了一种方法，试图将这两种方法结合起来，形成一种对噪声和初始化不良具有鲁棒性的统一方法。作者的方法使用格林定理推导出图像中同质区域分类区域的边界，并将其与基于灰度梯度的边界发现器相结合。这将边缘/形状信息的感知概念与灰度同质性结合起来。在大脑和心脏的合成和真实医学图像上进行了大量实验来评估新方法，结果表明，与传统的基于梯度的可变形边界发现相比，集成方法通常表现更好。此外，这种方法在计算开销增加很少的情况下获得了这些改进，这一优势来自于格林定理的应用。
