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The Scientific World Journal
Volume 2015, Article ID 823541, 12 pages
Research Article

Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes

1Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai 600100, India
2Department of Electronics and Communication Engineering, Alliance University, Bangalore 562106, India

Received 28 April 2015; Accepted 21 October 2015

Academic Editor: Qingfu Zhang

Copyright © 2015 Maya Eapen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.