Dept. of Biophys. and Electron. Eng., Genoa Univ.
IEEE Trans Image Process. 1996;5(3):429-46. doi: 10.1109/83.491317.
We describe a framework for multivalued segmentation and demonstrate that some of the problems affecting common region-based algorithms can be overcome by integrating statistical and topological methods in a nonlinear fashion. We address the sensitivity to parameter setting, the difficulty with handling global contextual information, and the dependence of results on analysis order and on initial conditions. We develop our method within a theoretical framework and resort to the definition of image segmentation as an estimation problem. We show that, thanks to an adaptive image scanning mechanism, there is no need of iterations to propagate a global context efficiently. The keyword multivalued refers to a result property, which spans over a set of solutions. The advantage is twofold: first, there is no necessity for setting a priori input thresholds; secondly, we are able to cope successfully with the problem of uncertainties in the signal model. To this end, we adopt a modified version of fuzzy connectedness, which proves particularly useful to account for densitometric and topological information simultaneously. The algorithm was tested on several synthetic and real images. The peculiarities of the method are assessed both qualitatively and quantitatively.
我们描述了一种多值分割框架，并证明通过以非线性方式整合统计和拓扑方法，可以克服一些常见基于区域的算法所面临的问题。我们解决了对参数设置的敏感性、处理全局上下文信息的困难以及结果对分析顺序和初始条件的依赖性。我们在理论框架内开发我们的方法，并将图像分割定义为估计问题。我们表明，由于自适应图像扫描机制，无需迭代即可有效地传播全局上下文。“多值”一词是指一种跨越一组解决方案的结果属性。其优势有二：首先，无需预先设置输入阈值；其次，我们能够成功应对信号模型不确定性的问题。为此，我们采用了一种改进的模糊连接性，这对于同时考虑密度和拓扑信息非常有用。该算法已在多个合成和真实图像上进行了测试。该方法的特点从定性和定量两个方面进行了评估。
