Tremendous efforts have been made in the evaluation of images to extract organs, vessels, or objects. Image segmentation succeeded only under certain constraints. A more realistic approach is based on the acceptance of the fuzziness of image data, i.e., coronary angiograms provide (nearly precise) information on anatomy and perfusion. SPECT and PET scans reveal (fuzzy) information on blood flow and metabolic functions within an organ. A fusion of these modalities needs a normalization procedure, i.e., mapping to the same type of information, either precise or fuzzy. This paper describes segmentation and fusion processes which are based on successive approximation guided by mathematical morphology procedures and supported by neural networks and fuzzy inference. Results are obtained from myocardial images as well as from lever images.