We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in
medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR).
The AAR system we are developing includes five main parts: model building, object recognition, object delineation,
pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling
part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid
strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM)
method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for
object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method
to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information
embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost
function, which effectively integrates the specific image information with the ASM shape model information. The
proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to
explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method
improves on ASM and GC and can provide practical operational time on clinical images.