ArticleFrontiers in physiology2022
Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Impact of Simulated Artifacts on the Classification Performance of Apical Views in Transthoracic Echocardiography Using Convolutional Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- Preliminary exploration on using entropy-weighted hybrid pooling in CNN for ultrasound breast cancer detection.Frontiers in oncology · 2026Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.Scientific reports · 2024Article
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Authors and funding
6 authors.
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Abstract
Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.
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