文献类型：期刊文献

英文题名：A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques

作者：Qiu, Chenghao[1]; Huang, Zifan[2]; Lin, Cong[2]; Zhang, Guodao[3]; Ying, Shenpeng[4]

机构：[1] School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610000, China; [2] School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China; [3] Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; [4] Department of Radiotherapy, Taizhou Central Hospital [Taizhou University Hospital], Taizhou, 318000, China

年份：2023

卷号：166

外文期刊名：Computers in Biology and Medicine

收录：EI(收录号：20234214904402)、Scopus(收录号：2-s2.0-85173899699)

语种：英文

外文关键词：Diseases - Image denoising - Image enhancement - Image segmentation - Medical imaging - Noise abatement - Speckle - Textures - Tumors - Ultrasonics

外文摘要：The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image texture features, impacting clinical judgment. Thus, maintaining clear lesion boundaries while eliminating speckle noise is a challenging task. This paper presents an innovative approach for denoising ultrasound images using a novel noise reduction network model called content-aware prior and attention-driven (CAPAD). The model employs a neural network to automatically capture the hidden prior features in ultrasound images to guide denoising and embeds the denoiser into the optimization module to simultaneously optimize parameters and noise. Moreover, this model incorporates a content-aware attention module and a loss function that preserves the structural characteristics of the image. These additions enhance the network's capacity to capture and retain valuable information. Extensive qualitative evaluation and quantitative analysis performed on a comprehensive dataset provide compelling evidence of the model's superior denoising capabilities. It excels in noise suppression while successfully preserving the underlying structures within the ultrasound images. Compared to other denoising algorithms, it demonstrates an improvement of approximately 5.88% in PSNR and approximately 3.61% in SSIM. Furthermore, using CAPAD as a preprocessing step for breast tumor segmentation in ultrasound images can greatly improve the accuracy of image segmentation. The experimental results indicate that the utilization of CAPAD leads to a notable enhancement of 10.43% in the AUPRC for breast cancer tumor segmentation. ? 2023