In this paper we present a new method for tagging image regions using uncertain information granules. This tagging forms an efficient route for the elicitation of knowledge from domain experts with respect to images. We then use this uncertain granular information to train a fuzzy machine learner and then to classify unseen images. This method is particularly suited to applications where expert input into the classification process is essential but where the expert's time is in extremely short supply. Results are presented within the example domain of detecting lung disease from Computed Tomography scans.