Speckle noise removal in medical ultrasonic image using spatial filters and DnCNN

Medical ultrasonic imaging is affected by an inherent phenomenon called speckle noise, which prevents the identification of details in images. While several state-of-the-art methods have been already proposed for speckle noise reduction, they often suffer from blurring, artifacts, and losing the useful details and features of image which limits the accuracy of medical diagnosis. To address such challenges, in this paper, taking the advantage of convolutional neural network (CNN), a hybrid algorithm composed of anisotropic spatial filter and denoising CNN (DnCNN) is proposed for speckle noise reduction. To further eliminate the blurring effect and increase the contrast of image edges, we incorporate Wiener filter and fast local Laplacian filter as post-processing. The experimental results on medical images show that the proposed method, in addition to an effective noise suppression, can preserve the edges and structural details of the image. The proposed algorithm outperforms state-of-the-art noise removal filters, including Frost, Lee, Median, the speckle reducing anisotropic diffusion (SRAD) filter, Wiener filter, DnCNN, and fusion filters including SRAD + DnCNN, and SRAD + DnCNN + Wiener, in terms of PSNR, SSI, and SSIM metrics.

Automated summary

This paper proposes a hybrid algorithm using a convolutional neural network (CNN) to reduce speckle noise in medical ultrasonic imaging. Experimental results show that the proposed method outperforms other methods in terms of accuracy and preservation of image details.