Department of Information Management, Chang Gung University, Taiwan, R.O.C.
IEEE Trans Image Process. 2003;12(9):1007-15. doi: 10.1109/TIP.2003.815258.
Of the many proposed image segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as symmetric region growing algorithms, leads to a single-pass region-growing algorithm applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Results illustrate the method's efficiency and its application to 3D medical image segmentation.
在众多提出的图像分割方法中，区域生长法一直是最受欢迎的方法之一。然而，关于区域生长的研究主要集中在特征度量的设计以及生长和合并准则上。这些方法中的大多数都对检查点和区域的顺序具有固有依赖性。这种弱点意味着所需的分割结果对初始生长点的选择很敏感。我们为一类对初始生长点选择不敏感的区域生长算法定义了一组理论标准。此类算法称为对称区域生长算法，可用于任何维度的图像的单遍区域生长算法。此外，它们还可以得出既节省内存又节省计算资源的区域生长算法。结果说明了该方法的效率及其在 3D 医学图像分割中的应用。
