The problem of image segmentation is often considered in the framework of graphs. In this context, two main paradigms exist: in the first, the vertices of a non-directed graph represent the pixels (leading e.g. to the watershed, the random walker or the graph cut approaches); in the second, the vertices of a directed graph represent the connected regions, leading to the so-called morphological trees (e.g. the component-trees or the trees of shapes). Various approaches have been proposed for carrying out segmentation from images modeled by such morphological trees, by computing cuts of these trees or by selecting relevant nodes from descriptive attributes. In this article, we propose a new way of carrying out segmentation from morphological trees. Our approach is dedicated to take advantage of the morphological tree of an image, enriched by multiple attributes in each node, by using maximally stable extremal regions and random walker paradigms for defining an optimal cut leading to a final segmentation. Experiments, carried out on multimodal medical images emphasize the potential relevance of this approach.

通常在图的框架中考虑图像分割的问题。在这种情况下，存在两个主要范式：首先，无向图的顶点表示像素（例如，导致分水岭，随机游走或图割入）。在第二个图中，有向图的顶点表示连接的区域，通向所谓的形态树（例如，成分树或形状树）。已经提出了各种方法来从由这种形态树建模的图像，通过计算这些树的切割或通过从描述属性中选择相关节点来进行分割。在本文中，我们提出了一种从形态树进行分割的新方法。我们的方法致力于利用图像的形态树，通过使用最大稳定的极值区域和随机沃克范式来定义导致最终分割的最优切割，从而在每个节点中通过多个属性来丰富这些属性。在多模式医学图像上进行的实验强调了这种方法的潜在相关性。