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BioMed Research International
Volume 2014 (2014), Article ID 198015, 12 pages
Research Article

A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy

1Department of Electrical Engineering at the Florida International University, Miami, FL 33174, USA
2Herbert Wertheim College of Medicine at the Florida International University, Miami, FL 33174, USA
3Biomedical Engineering Department at the Florida International University, Miami, FL 33174, USA

Received 16 December 2013; Revised 31 May 2014; Accepted 10 June 2014; Published 3 July 2014

Academic Editor: Hidetaka Arimura

Copyright © 2014 Mohammed Goryawala 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.


This study introduces a novel liver segmentation approach for estimating anatomic liver volumes towards selective internal radiation treatment (SIRT). The algorithm requires minimal human interaction since the initialization process to segment the entire liver in 3D relied on a single computed tomography (CT) slice. The algorithm integrates a localized contouring algorithm with a modified k-means method. The modified k-means segments each slice into five distinct regions belonging to different structures. The liver region is further segmented using localized contouring. The novelty of the algorithm is in the design of the initialization masks for region contouring to minimize human intervention. Intensity based region growing together with novel volume of interest (VOI) based corrections is used to accomplish the single slice initialization. The performance of the algorithm is evaluated using 34 liver CT scans. Statistical experiments were performed to determine consistency of segmentation and to assess user dependency on the initialization process. Volume estimations are compared to the manual gold standard. Results show an average accuracy of 97.22% for volumetric calculation with an average Dice coefficient of 0.92. Statistical tests show that the algorithm is highly consistent and independent of user initialization ( and Fleiss’ ).