Local morphological granulometries are generated by opening an image successively by an increasing family of structuring elements and, at each pixel, keeping an image area count in a fixed-size window about the pixel. After normalization there is at each pixel a probability density, called a `local pattern spectrum,' and the moments of this density are used to classify the pixel according to surrounding texture. The method having been developed for binary images, the present paper applies a gray-scale version of the methodology to detect osteoporosis in magnetic resonance (MR) images of the wrist. Maximum-likelihood classification is used to apply the local-pattern-spectra moment information. Owing to the presence of a continuous intertwined network of bone fibers called trabeculae, when imaged by an MR imaging system a normal region of bone tissue possesses a coarse, grainy texture resulting in characteristic granulometric features. Osteoporosis is a metabolic bone disease typified by a gradual loss of trabecular bone, and this loss is revealed by significant changes in the granulometric features, thereby leading to detection.