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

Journal of Biomedical Science and Engineering 2014年第12期7卷 994-1004页

作者： Walita Narkbuakaew Hiroshi Nagahashi Kota Aoki yoshiki kubota Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology Kanagawa Japan Imaging Science and Engineering Laboratory Tokyo Institute of Technology Kanagawa Japan Gunma University Heavy-Ion Medical Center Gunma Japan

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.

关键词： Liver Segmentation Level-Set Geodesic Active Contour Speed Images Statistical Thresholds

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