This paper describes the application of a new texture characterization algorithm for the segmentation of medical ultrasound images. The morphology of these images poses significant problems for the application of traditional image processing techniques and their analysis has been the subject of research for several years. The basis of the algorithm is an optimum signal modelling algorithm (Least Mean Squares-based), which estimates a set of parameters from small image regions. The algorithm has been converted to a structure suitable for implementation on a Parallel Virtual Machine (PVM) consisting of a Network of Workstations (NoW), to improve processing speed. Tests were initially carried out on standard textured images. This paper describes preliminary results of the application of the algorithm in texture discrimination and segmentation of medical ultrasound images. The images examined are primarily used in the diagnosis of carotid plaques, which are linked to the risk of stroke.