CIBM-Siemens, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland.
Med Image Anal. 2011 Dec;15(6):830-9. doi: 10.1016/j.media.2011.05.002. Epub 2011 May 13.
Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.
马尔可夫随机场图像分割模型的推断通常使用迭代方法来完成，这些方法适用于独立混合模型的著名期望最大化（EM）算法。然而，其中一些自适应方法是特定的，并且可能在数值上不稳定。在本文中，我们回顾了三种类似于 EM 的马尔可夫随机场分割变体，并在理论和实践层面上比较了它们的收敛特性。我们特别提倡一种涉及异步体素更新的数值方案，为此可以建立一般的收敛结果。我们在磁共振图像中的脑组织分类实验提供了证据，表明该算法的收敛速度可能明显快于其竞争对手，同时产生至少同样好的分割结果。
