Abstract Motivation: Images in cellular and molecular biology (from microscopy, blots, biochips, etc.) are often disturbed, so that the detection and analysis of the respective relevant geometrical objects may be difficult or error-prone. The disturbances are either caused by the detector, usually a CCD camera, or by the experimental setup. Furthermore, microtechnology experiments often require simultaneous multiple-colour stainings. Therefore, the image analysis of such experiments should be colour-sensitive, and colour shadings should not only be detectable but also quantifiable. Results: Here, we describe a general solution as applied to the analysis of blots and DNA chips as well as to microscopy images of tissues. We decided to use (i) a stochastic filter as used by Wiener for image segmentation as the starting point for object detection, (ii) chaincodes as described by Freeman for object description, (iii) a novel ‘rolling disc algorithm’ to spot the objects to be analysed and (iv) an HSI instead of an RGB colour model for colour analysis. With this combination we succeeded in performing shape detection and colour-based analysis of disturbed images. Availability: The corresponding modules (C++) are available on request.