Scherrer Benoit, Dojat Michel, Forbes Florence, Garbay Catherine
Grenoble Institut des Neurosciences, Centre de Recherche Institut national de la santé et de la recherche medicale U 836, E5 Chemin Fortuné Ferrini, La Tronche, BP 170, 38042 Grenoble Cedex 09, France.
Artif Intell Med. 2009 May;46(1):81-95. doi: 10.1016/j.artmed.2008.08.012. Epub 2008 Oct 16.
Markov random field (MRF) models have been traditionally applied to the task of robust-to-noise image segmentation. Most approaches estimate MRF parameters on the whole image via a global expectation-maximization (EM) procedure. The resulting estimated parameters are likely to be uncharacteristic of local image features. Instead, we propose to distribute a set of local MRF models within a multiagent framework.
Local segmentation agents estimate local MRF models via local EM procedures and cooperate to ensure a global consistency of local models. We demonstrate different types of cooperations between agents that lead to additional levels of regularization compared to the standard label regularization provided by MRF. Embedding Markovian EM procedures into a multiagent paradigm shows interesting properties that are illustrated on magnetic resonance (MR) brain scan segmentation.
A cooperative tissue and subcortical structure segmentation approach is designed with such a framework, where both models mutually improve. Several experiments are reported and illustrate the working of Markovian EM agents. The evaluation of MR brain scan segmentation was performed using both phantoms and real 3T brain scans. It showed a robustness to intensity non-uniformity and noise, together with a low computational time.
Based on these experiments MRF agent-based approach appears to be a very promising new tool for complex image segmentation.
马尔可夫随机场（MRF）模型传统上已应用于抗噪声图像分割任务。大多数方法通过全局期望最大化（EM）过程在整个图像上估计MRF参数。由此得到的估计参数可能无法表征局部图像特征。相反，我们建议在多智能体框架内分布一组局部MRF模型。
局部分割智能体通过局部EM过程估计局部MRF模型，并进行协作以确保局部模型的全局一致性。我们展示了智能体之间不同类型的协作，与MRF提供的标准标签正则化相比，这些协作导致了更高层次的正则化。将马尔可夫EM过程嵌入多智能体范式显示出有趣的特性，这些特性在磁共振（MR）脑扫描分割中得到了说明。
利用这样一个框架设计了一种协作式组织和皮质下结构分割方法，其中两个模型相互改进。报告了几个实验，并说明了马尔可夫EM智能体的工作情况。使用模型体模和真实3T脑扫描对MR脑扫描分割进行了评估。结果表明，该方法对强度不均匀性和噪声具有鲁棒性，且计算时间较短。
基于这些实验，基于MRF智能体的方法似乎是一种非常有前途的复杂图像分割新工具。
