Application research of CT image enhancement based on improved SRGAN network

At present, intervertebral disc diseases can be diagnosed by observing CT images or MRI images. Compared with MRI images, CT images have low cost and fast film forming speed, but there are some problems, such as low contrast, fuzzy focus area of intervertebral disc, unclear edge and so on. To solve the above problems, an improved CT image enhancement method based on SRGAN network is proposed. In this method, the adaptive segmentation and fusion method is used for image preprocessing, the BN layer is removed from the SRGAN generator, and the attention mechanism is introduced to make each residual block generate the feature map to obtain the corresponding weight. At the same time, the boundary loss function is added to make the reconstructed lesion area clearer and the edge more obvious. This method is tested on the real head and neck CT images and MRI images provided by Henan people's hospital. The classical image enhancement algorithm is compared with the latest image enhancement algorithm to objectively evaluate the enhanced CT images. At the same time, two clinicians subjectively evaluate the enhanced CT images through the 5-point image quality evaluation standard. The results show that this method significantly improves the SSIM, PSNR, information entropy, edge intensity and average gradient of CT image, makes the focus area of CT image clearer and the edge more obvious, which is convenient for doctors to read and diagnose, and has strong application value.