Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 31-36, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-31-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 31-36, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-31-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

AUTOMATED EXTRACTION OF LIVER OUTLINES FROM COMPUTED TOMOGRAPHY SCAN IMAGES USING A CUDA-BASED SEGMENTATION METHOD

Y. Chen, D. Li, Q. Zhu, C. Wang, and J. Li Y. Chen et al.
  • Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China

Keywords: Live Outline, CT Scan, Object Extraction, Image Segmentation, Hepatic Segmentation, Vascular Extraction

Abstract. The traditional fast marching algorithm for segmentation of the liver is suitable for processing on the central processing unit (CPU) platform, however, it is not suitable for implementation on Graphics Processing Unit (GPU). The fuzzy connection algorithm is used to extract the blood vessels in the liver, but there is a calculation error. The refinement algorithm is very time consuming when extracting the target skeleton line from the 3D image. In this paper, the fast-marching algorithm and the thinning algorithm are improved, which can be applied to the GPU computing, The fuzzy algorithm is also improved, and the calculation error of the algorithm is solved, making it more suitable for medical image processing. The computing speed of GPU is far faster than CPU. Medical image processing is one of the earliest applications where the computing performance is improved by GPU. These three segmentation methods, fast marching method, fuzzy connecting method and refinement algorithm are very common in medical image segmentation. Because the increment of medical image data results in the extension of computing time for medical image processing, it is necessary to apply the high parallelism of the GPU to speed up these algorithms. The experiment results demonstrate the feasibility of our accelerating algorithm.