Ultrasound Breast images denoising using Generative Adversarial Networks (GANs)

Ultrasound imaging plays an important role in screening early detection and breast cancer diagnosis. However, speckle noise affects medical ultrasound images and degrades the visual radiologic evaluation, which generally causes several difficulties in identifying the malignant and benignant regions. Denoising is an important step in preprocessing medical images, because it restores the maximum details preserving edges and all information of the images, achieving successful accuracy in anomalies classification. To reduce the speckle noise and retain image features well, we proposed two GANs models to breast ultrasound speckle denoising as preprocessing image, (i) Conditional GAN and (ii) WGAN. The better denoising image quality was measured by peak signal to noise ratio (PSNR) and structural similarity index (SSIM). The experimental analysis clearly shows that the CGAN method achieves better visual image quality in terms of PSNR=38.18 dB and SSIM= 0.96 with respect to WGAN model (PSNR=33.0068 dB and SSIM=0.91) on the small Ultrasound training datasets. Thus, we conclude that GANs can help in denoising ultrasound medical imaging, and as a future work these data can be used as computer system input for image segmentation and classification, reducing the hand-dependence and helping radiologists to improve breast cancer detection.