We study the problem of automatic delineation of an anatomic object in an image, where the object is solely
identified by its anatomic prior. We form such priors in the form of fuzzy models to facilitate the segmentation of
images acquired via different imaging modalities (like CT, MRI, or PET), in which the recorded image properties
are usually different. Our main interest is in delineating different body organs in medical images for automatic
anatomy recognition (AAR).
The AAR system we are developing consists of three main components: (C1) building body-wide groupwise
fuzzy anatomic models; (C2) recognizing the body organs geographically and then delineating them by employing
the models; (C3) generating quantitative descriptions. This paper focuses on (C2) and presents a unified approach
for model-based segmentation within which several different strategies can be formulated, ranging from modelbased
hard/fuzzy thresholding to model-based graph cut, fuzzy connectedness, and random walker methods and
algorithms. This is an important theoretical advance.
The presented experiments clearly prove, that a fully automatic segmentation system based on the fuzzy
models can indeed provide the reliable segmentations. However, the presented experiments utilize only the
simplest versions of the methodology presented in the theoretical part of the paper. The full experimental
evaluation of the methodology is still a work in progress.