A novel fast liver segmentation method with graph cuts
Fan Yang, Weimin Zhai, Yannan Zhao, Hong Wang, Peifa Jia · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Liver segmentation remains a difficult problem in medical images processing, especially when accuracy and speed are both seriously considered. Graph Cuts is a powerful segmentation tool through which the optimal results are got by considering both region and boundary information in images. However, the traditional Graph Cuts algorithms are always computationally expensive and inappropriate to be applied to real clinical circumstance. Recently, the GPU (Graphics Processor Unit) had evolved to be a cheap and superpower general purpose computing instrument, especially when NVIDIA released its revolutionary CUDA (Compute Unified Device Architecture). In this paper, we introduce a novel method to segment 3D liver images with GPU, using the Push-Relable style 3D Graph Cuts implementation. Some modifications such as 3D storage structures are also introduced which make our implement well fit to the GPU parallel computing capabilities. Experiments have been executed on human liver CT data and these experiments show that our method can obtains results in much less time compared to the implement with CPU.