Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (3): 332-336.DOI: 10.12068/j.issn.1005-3026.2020.03.006

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Generating Algorithm of Medical Image Simulation Data Sets Based on GAN

MENG Lu1, ZHONG Jian-ping1, LI Nan2   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China;2. Shenyang Product Quality Supervision and Inspection Institute, Shenyang 110000, China.
  • Received:2019-06-17 Revised:2019-06-17 Online:2020-03-15 Published:2020-04-10
  • Contact: MENG Lu
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Abstract: Based on generative adversarial networks (GAN), a deep learning algorithm for generating diseased liver CT image data sets was proposed. Firstly, the CT image data file was formatted and saved as an image file in PNG format. Then the liver lesion area was uniformly marked as white, and the liver CT original image was combined to form a paired picture. Finally, diseased liver image was generated using a pix2pix architecture that created an anti-network. In order to quantitatively analyze and compare the generated image with the target image, the peak signal-to-noise ratio and structural similarity were used to evaluate the model. The results showed that the average peak signal-to-noise ratio of the simulated CT diseased liver image generated by the proposed algorithm is 64.72dB and the average structural similarity is 0.9973, thus proving these simulated image data have very high trueness.

Key words: generative adversarial networks(GAN), image processing, liver image simulation, parameter adjustment, data augmentation

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