A novel fast liver segmentation method with graph cuts

Fan Yang; Weimin Zhai; Yannan Zhao; Hong Wang; Peifa Jia

doi:10.1117/12.844218

12 March 2010 A novel fast liver segmentation method with graph cuts

Fan Yang, Weimin Zhai, Yannan Zhao, Hong Wang, Peifa Jia

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76233Y (2010) https://doi.org/10.1117/12.844218
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

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.

Citation Download Citation

Fan Yang, Weimin Zhai, Yannan Zhao, Hong Wang, and Peifa Jia "A novel fast liver segmentation method with graph cuts", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233Y (12 March 2010); https://doi.org/10.1117/12.844218

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