CNN and multi-feature extraction based denoising of CT images

In the past decades, Computed Tomography (CT) images have been widely used and played a critically role in medical diagnosis. In low-dose CT images, reducing the radiation dose can reduce damage to patients, but at the same time, the projected image is contaminated with noise, resulting in a lot of noise in the reconstructed medical CT image, which can affect the clinical diagnosis. Based on the network structure of GoogleNet and Inception series, and combined with deep residual learning and convolutional neural networks, a novel denoising method with multi-feature extraction is proposed in this paper for medical CT images. The extraction of shallow multi-features in medical CT images is achieved by combining convolution filters of different sizes. This is useful for obtaining more detailed feature information in the image. Through the fusion of image features, the noise learning in medical CT images is realized. The developed neural network is more targeted to the removal of noise in the medical CT image. Experimental results show the denoised medical CT images can better retain edge and texture area details with the proposed method. Compared with the existing methods, the method proposed in this paper improves the denoising effect of medical CT images.