  Recurrence plot based methods are highly efficient and widely accepted tools
for the investigation of time series or one-dimensional data. We present an
extension of the recurrence plots and their quantifications in order to study
recurrent structures in higher-dimensional spatial data. The capability of this
extension is illustrated on prototypical 2D models. Next, the tested and proved
approach is applied to assess the bone structure from CT images of human
proximal tibia. We find that the spatial structures in trabecular bone become
more self-similar during the bone loss in osteoporosis.
