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.