An Efficient Liver-Segmentation System Based on a Level-Set Method and Consequent Processes

Walita Narkbuakaew¹, Hiroshi Nagahashi², Kota Aoki², Yoshiki Kubota³

¹Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Kanagawa, Japan.
²Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Kanagawa, Japan.
³Gunma University Heavy-Ion Medical Center, Gunma, Japan.

DOI: 10.4236/jbise.2014.712097                

Abstract

This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.

Keywords

Liver Segmentation, Level-Set, Geodesic Active Contour, Speed Images, Statistical Thresholds

Conflicts of Interest

The authors declare no conflicts of interest.

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