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 model-based 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.

Conference Proceedings reprint.

Last modified April 8, 2013.