Work

Incorporation of Structural Similarity Index and Regularization Term into Neighbor2Neighbor Unsupervised Learning Model for Efficient Ultrasound Image Data Denoising

Year: 2024

Type: article

Abstract: Medical ultrasound imaging is extensively employed for diagnostic purposes. However, image quality remains a major obstacle to achieving greater accuracy. Conventional supervised deep learning denoising methods often rely on matched noise-free and noisy image pairs, which can be highly challenging in practical ultrasound applications. Moreover, due to the limitations associated with independent noise, existing unsupervised denoising methods such as Neighbor2Neighbor are unable to efficiently address correlated noise in ultrasound images. Meanwhile, these methods utilize a random neighborhood downsampling technique, frequently resulting in pixel loss. Hence, this study proposes a novel Neighbor2Neighbor algorithm, which reconstructs ultrasound images by improving the downsampling approach. Moreover, it incorporates a structural similarity index and a regularization term, thereby enhancing its ability to suppress both independent and correlated noise. Extensive experiments on an ultrasound image dataset demonstrate that the proposed Neighbor2Neighbor algorithm outperforms the state-of-the-art baseline algorithms in peak signal-to-noise ratio (PSNR), mean structural similarity index measure (MSSIM), feature similarity index measure (FSIM), and edge preservation index (EPI).

Source: Applied Sciences

DOI: 10.3390/app14177988

Authors: Peiyang Wei, Liping Wang, Jianhong Gan, Xiaoyu Shi, Mingsheng Shang

Institutions: Chongqing University of Posts and Telecommunications, Chinese Academy of Sciences, Chengdu University of Information Technology, Chongqing Institute of Green and Intelligent Technology, Chengdu Neusoft University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,728