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

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 ba...

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Bibliographic Details
Published in:TheScientificWorld 2015, Vol.2015 (2015), p.1-12
Main Authors: Eapen, Maya, Geetha, G., Korah, Reeba
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computed tomography
CT imaging
Energy
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging, Three-Dimensional - methods
Intelligence
Knowledge
Liver
Liver - diagnostic imaging
Media coverage
Methods
Neighborhoods
Swarm intelligence
Tomography, X-Ray Computed - methods
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Summary: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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2015/823541