Abstract

Info

Title:: Convolutional neural network-based de-speckling algorithm for ultrasound images

Author(s):: LIAO Lili; ZHANG Dong; School of Physics and Technology, Wuhan University, Wuhan 430072, China

Keywords:: Keywords: speckle noise convolutional neural network texture information simulated ultrasound imaging technology

Abstract:: Abstract: In order to improve the quality of ultrasound images and solve the problems of how to maintain the balance between suppressing speckle noise and retaining the texture features in ultrasound images, a de-speckling algorithm based on convolutional neural network (DSCNN) is proposed. The proposed algorithm utilizes the powerful fitting ability of CNN to learn the complex mapping from the ultrasound image to its corresponding high-quality image. Meanwhile, it reduces the loss of texture information and the blurred details during denoising by advanced loss function. Different from previous assumption that the speckle noise is a kind of multiplicative noise, the simulated ultrasound imaging technology based on the ultrasound image acquisition model and the speckle noise formation model is used to generate more realistic ultrasound images which are taken as the training data for denoising model, thus solving the problems of lack of training data and overcoming difficulties in obtaining a noise-free image which is used as a label in spatial registration with the ultrasound image. Compared with the images denoised by other typical denoising algorithms, the ultrasound image denoised by DSCNN wins higher scores both in visual effect and image quality assessment, and SSIM even reaches 0.856 9, which is highest among all methods discussed in the study.