  We study the applicability of a set of texture descriptors introduced in
recent work by the author to texture-based segmentation of images. The texture
descriptors under investigation result from applying graph indices from
quantitative graph theory to graphs encoding the local structure of images. The
underlying graphs arise from the computation of morphological amoebas as
structuring elements for adaptive morphology, either as weighted or unweighted
Dijkstra search trees or as edge-weighted pixel graphs within structuring
elements. In the present paper we focus on texture descriptors in which the
graph indices are entropy-based, and use them in a geodesic active contour
framework for image segmentation. Experiments on several synthetic and one
real-world image are shown to demonstrate texture segmentation by this
approach. Forthermore, we undertake an attempt to analyse selected
entropy-based texture descriptors with regard to what information about texture
they actually encode. Whereas this analysis uses some heuristic assumptions, it
indicates that the graph-based texture descriptors are related to fractal
dimension measures that have been proven useful in texture analysis.
