Chen Xie
ABSTRACT
Few studies present how to implement super resolution (SR) on abdominal Computed Tomography (CT) images. However, in the field of medical imaging, the development of an advanced SR system will potentially improve the clinical diagnosis and prognosis of various diseases, enabling the medical image generation machine to communicate its information better. The efficient sub-pixel convolutional network (ESPCN) is considered as the baseline network. Successfully implementing a residual learning strategy and proposing an approach for incorporating categorical conditions as the prior in the baseline, we proposed three novel deep residual learning based networks to restore more realistic texture for pancreatic tumour regions in abdominal CT images. The quality of super-resolved images is evaluated quantitatively by using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our proposed networks show better SR performance compared with other existing deep learning based methods on the abdominal CT dataset.
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Index Terms
- Novel Approaches of Abdominal Computed Tomography Image Super Resolution based on Deep Residual Learning
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