The detection of suspicious cancerous regions is still a problematic task in histopathology, where complex qualitative, and highly subjective, analyses are required by experts. Digital pathology is the option for building semi-automated tools that could assist pathologists in carrying out their analysis in a quantitative way. Methods for assisted detection of cancerous areas are mostly based on low level textural features of the tissue, whose semantic level is far from the visual appearance that histopathologists consider during their analysis. In order to bridge the semantic gap between histopathology and machine representation, we propose an algorithm for the detection of cancerous regions in lung and bladder adenocarcinoma samples, based on a supervised multi-level representation directly linked to histopathological characteristics. Instead, our unsupervised clustering method performs a segmentation of the histopathology structures according to their visual appearance through a similarity metric based on histograms of samples in the Lab perceptive color space. This permits to increase the sensitivity of the supervised approach by extending the regions (i.e., hits) it detects. We validated the accuracy of the proposed segmentation approach, using a group of ten users using 40 histopathology cases, showing a good response. The experiments, performed using the ground truth provided by a board of certified experts on different samples of adenocarcinoma (graded G1), prove the effectiveness of our approach both in terms of sensitivity and precision in detecting suspicious regions. Our algorithm is currently under testing on more samples and different cancerous histotypes.