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

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

机构：[1]Univ Elect Sci & Technol China, Sch Life Sci & Technol, Chengdu 610000, Sichuan, Peoples R China;[2]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Hangzhou Dianzi Univ, Dept Digital Media Technol, Hangzhou 310018, Peoples R China;[4]Taizhou Cent Hosp, Taizhou Univ Hosp, Dept Radiol, Taizhou 318000, Peoples R China

年份：2023

卷号：166

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：SCI-EXPANDED(收录号：WOS:001098246000001)、、WOS

基金：This work was supported in part by the National Natural Science Foundation of China (62272109) , Undergraduate Innovation Team Project of Guangdong Ocean University (CXTD2021019) , Fundamental Research Funds for the Provincial Universities of Zhejiang (GK239909299001-019) , and the Research Foundation of Hangzhou Dianzi University (KYS335622091; KYH333122029M) .

语种：英文

外文关键词：Ultrasound image; Speckle denoising; Image prior; Attention mechanisms

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.