  Recent research on spatial and spatio-temporal model checking provides novel
image analysis methodologies, rooted in logical methods for topological spaces.
Medical Imaging (MI) is a field where such methods show potential for
ground-breaking innovation. Our starting point is SLCS, the Spatial Logic for
Closure Spaces -- Closure Spaces being a generalisation of topological spaces,
covering also discrete space structures -- and topochecker, a model-checker for
SLCS (and extensions thereof). We introduce the logical language ImgQL ("Image
Query Language"). ImgQL extends SLCS with logical operators describing distance
and region similarity. The spatio-temporal model checker topochecker is
correspondingly enhanced with state-of-the-art algorithms, borrowed from
computational image processing, for efficient implementation of distancebased
operators, namely distance transforms. Similarity between regions is defined by
means of a statistical similarity operator, based on notions from statistical
texture analysis. We illustrate our approach by means of two examples of
analysis of Magnetic Resonance images: segmentation of glioblastoma and its
oedema, and segmentation of rectal carcinoma.
