In this paper we consider the problem of unsupervised boundary localization in textured images, reporting a texture separation algorithm which extracts textural density gradients by a non-linear multiple scale-space analysis of the image. Mathematical morphology is adopted at two stages of the algorithm: firstly the scale-space analysis is modeled by a differential morphological filter, and secondly, texture boundaries are extracted by segmenting the images resulting from a multi-scale morphological gradient operation applied to detail images. The segmentation stage consists of a parallel hierarchical clustering algorithm, aimed at the minimization of a global cost functional taking into account region homogeneity and segmentation quality. Experiments on Brodatz textures and real images are reported.