This paper presents a comparative study of several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods, and a novel approach based on the recently introduced concept of the frequency histogram of connected elements (FHCE). The paper first introduces the parameters that define a connected element and then details the sensitivity analysis of these parameters, showing that the grayscale intensity histogram of a digital image is a particular case of the FHCE. The application domain chosen for comparison purposes is the problem of medical images segmentation and, more specifically, as a particularly illustrative case the segmentation of digital angiograms is analyzed in detail. To get a comparative evaluation of FHCE performance, two well-established adaptive or contextual Bayesian segmentation algorithms have been applied to the segmentation of digital angiograms as well. The paper ends with a brief discussion of the comparative performances.