Abstract : Using the a-contrario framework recently introduced in the modeling of human visual perception, we build a statistical model to predict the detectability of a spot on a textured background. Contrary to classical formalisms (ideal observer and its extensions), which assume a known probability distribution for the signal to be detected, the a-contrario observer we build only relies on gestalt-driven measurements and on an approximate representation of the background texture. It extends the scope of previous a-contrario detectors by using a non-i.i.d. naive model and a notion of local context. The models we propose are first validated theoretically in the case of powerlaw textures, which are, in particular, classical models for mammograms. Then, going to more general microtextures (colored noise processes), we compute the relationship between the size of a spot and the minimum contrast required to reach a given detectability threshold according to the a-contrario observer. Three main types of microtextures pop out from this characterization, and in particular low-frequency textures for which curiously enough, the contrast being given, the most salient spots are the smallest ones. Last, we illustrate the interest of the a-contrario observer for two real applications: the detectability of opacities in mammograms and the perception of stains on pieces of clothing.