Share Email Print

Proceedings Paper

3D automatic anatomy recognition based on iterative graph-cut-ASM
Author(s): Xinjian Chen; Jayaram K. Udupa; Ulas Bagci; Abass Alavi; Drew A. Torigian
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 27 February 2010
PDF: 8 pages
Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76251T (27 February 2010); doi: 10.1117/12.844551
Show Author Affiliations
Xinjian Chen, National Institute of Health (United States)
Jayaram K. Udupa, The Univ. of Pennsylvania School of Medicine (United States)
Ulas Bagci, The Univ. of Nottingham (United Kingdom)
Abass Alavi, The Univ. of Pennsylvania School of Medicine (United States)
Drew A. Torigian, The Univ. of Pennsylvania School of Medicine (United States)

Published in SPIE Proceedings Vol. 7625:
Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; Michael I. Miga, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?