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.
In this paper, we introduce a parallel algorithm to implement the Region Growing algorithms in GPU, with the purpose
of 3D organ segmentation. Extensive Experiments have been executed on human CT Data, and these experiments show
that the algorithms obtain accurate results with a speed about 10-20 times faster than the traditional methods on CPU.
Several improvements to the traditional region growing algorithms are also introduced in this paper. This method is
integrated in several surgery planning and surgery navigation systems and has achieved good clinical results.