Speckle noise reduction for medical ultrasound images based on cycle-consistent generative adversarial network

Medical ultrasound (US) images are corrupted by speckle noise, which can adversely affect the disease diagnosis and treatment. Recently, the cycle-consistent adversarial network (CycleGAN) has been used in the style transfer for both natural and medical images. In this study, we aim to develop a US despeckling method based on the CycleGAN by the style transfer between the speckled noisy data domain and noise-free data domain by forming a bi-directional universal mapping. The inputs of noisy and noise-free images are designed in the CycleGAN model. For simulation work, we use both the multiplicative model and the spatial impulse response model to obtain noisy images from noise-free images. However, noise-free US images are not clinically available. Hence, for the real US despeckling scenario, the clinical images of hearts, lymph nodes, and breast tumors are used as noisy images; and the high-quality images that are derived from the clinical images by despeckling with the Gabor-based anisotropic diffusion (GAD) and selected with a new metric named the edge-to-noise ratio, are used as the noise-free images. We compare our CycleGAN based denoising method with nine existing denoising methods, i.e., the speckle reduction anisotropic diffusion, GAD, non-local means, wavelet transform, unbiased non-local means, statistical nearest-neighbors, TVHTVM, improved non-local self-similarity measures, and generative adversarial network. Our method outperforms other methods by visual assessment and quantitative comparison, which demonstrates its superiority for noise reduction and detail preservation.