principle of hard structure definition, just a provision to Images are by nature fuzzy. Approaches to object information retain inaccuracies by itself does not guarantee the accuextraction from images should attempt to use this fact and racy of their retention. From this consideration, the relative retain fuzziness as realistically as possible. In past image segaccuracy of these strategies compared to those using hard mentation research, the notion of ''hanging togetherness'' of (binary) segmentation principles naturally becomes an imimage elements specified by their fuzzy connectedness has been portant issue, which remains largely unexplored in medical lacking. We present a theory of fuzzy objects for n-dimensional 3D imaging [6,7]. The principle of retention of data inaccudigital spaces based on a notion of fuzzy connectedness of racies as realistically as possible in object representations image elements. Although our definitions lead to problems of and subsequently in object renditions and analysis is unenormous combinatorial complexity, the theoretical results doubtedly the right stand. However, no formal framework allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms has yet been developed to handle object-related issues. for extracting a specified fuzzy object and for identifying all Traditionally, in volume rendering, the given image data fuzzy objects present in the image data. We demonstrate the are considered to represent an amorphous volume, the utility of the theory and algorithms in image segmentation emphasis being mainly on creating a rendition that depicts based on several practical examples all drawn from medical object structures represented in the image data. By bringimaging.