Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network
Ultrasound images are often affected by limited resolution, artifacts, and inherent speckle noise. To address these challenges, researchers have explored denoising approaches. Recently, deep learning methods have demonstrated distinct advantages in ultrasound image denoising. However, further improv...
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2024-01-01
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| author | Anparasy Sivaanpu Kumaradevan Punithakumar Rui Zheng Michelle Noga Dean Ta Edmond H. M. Lou Lawrence H. Le |
| author_facet | Anparasy Sivaanpu Kumaradevan Punithakumar Rui Zheng Michelle Noga Dean Ta Edmond H. M. Lou Lawrence H. Le |
| author_sort | Anparasy Sivaanpu |
| collection | DOAJ |
| description | Ultrasound images are often affected by limited resolution, artifacts, and inherent speckle noise. To address these challenges, researchers have explored denoising approaches. Recently, deep learning methods have demonstrated distinct advantages in ultrasound image denoising. However, further improvements are needed to preserve structural details, such as boundaries, edges, and margins. This paper proposes a hybrid CNN-transformer network called HCTSpeckle, an encoder-decoder network with a fusion block designed to enhance ultrasound images. The fusion block combines swin transformers to capture global modeling relationships, and convolutional neural networks to extract local modeling details. It is integrated into the encoder-decoder structure, allowing the model to focus on both local and global texture structural information. An improved swin block is also introduced into the network to improve robustness by extracting more significant features. HCTSpeckle was evaluated both quantitatively and qualitatively with clinical objectives using two public and two private datasets. Both results showed that HCTSpeckle significantly enhanced the ultrasound image quality and outperformed state-of-the-art methods in noise reduction and structure preservation across all four datasets. Compared to existing denoising methods, HCTSpeckle achieved notably faster performance in terms of complexity comparison, such as parameter counts, gigaFLOPs, and inference time. Moreover, this study assessed the effectiveness of HCTSpeckle for alveolar bone segmentation using dental images, demonstrating that HCTSpeckle significantly improved segmentation performance. Furthermore, an experienced radiologist blindly rated the 250 dental US images on a scale of 1 to 5, with 5 being the highest image quality, showing that HCTSpeckle consistently produced higher-quality images. |
| format | Article |
| id | doaj-art-8ef7fa16de624e758289311d7e2b5ae2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-8ef7fa16de624e758289311d7e2b5ae22024-11-22T00:01:40ZengIEEEIEEE Access2169-35362024-01-011216860716862510.1109/ACCESS.2024.349690710752631Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer NetworkAnparasy Sivaanpu0https://orcid.org/0009-0007-5189-7320Kumaradevan Punithakumar1https://orcid.org/0000-0003-3835-1079Rui Zheng2https://orcid.org/0000-0003-0391-2454Michelle Noga3https://orcid.org/0000-0001-5127-7374Dean Ta4https://orcid.org/0000-0001-6651-4491Edmond H. M. Lou5https://orcid.org/0000-0002-7531-8377Lawrence H. Le6https://orcid.org/0000-0003-1907-1258Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaSchool of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaUltrasound images are often affected by limited resolution, artifacts, and inherent speckle noise. To address these challenges, researchers have explored denoising approaches. Recently, deep learning methods have demonstrated distinct advantages in ultrasound image denoising. However, further improvements are needed to preserve structural details, such as boundaries, edges, and margins. This paper proposes a hybrid CNN-transformer network called HCTSpeckle, an encoder-decoder network with a fusion block designed to enhance ultrasound images. The fusion block combines swin transformers to capture global modeling relationships, and convolutional neural networks to extract local modeling details. It is integrated into the encoder-decoder structure, allowing the model to focus on both local and global texture structural information. An improved swin block is also introduced into the network to improve robustness by extracting more significant features. HCTSpeckle was evaluated both quantitatively and qualitatively with clinical objectives using two public and two private datasets. Both results showed that HCTSpeckle significantly enhanced the ultrasound image quality and outperformed state-of-the-art methods in noise reduction and structure preservation across all four datasets. Compared to existing denoising methods, HCTSpeckle achieved notably faster performance in terms of complexity comparison, such as parameter counts, gigaFLOPs, and inference time. Moreover, this study assessed the effectiveness of HCTSpeckle for alveolar bone segmentation using dental images, demonstrating that HCTSpeckle significantly improved segmentation performance. Furthermore, an experienced radiologist blindly rated the 250 dental US images on a scale of 1 to 5, with 5 being the highest image quality, showing that HCTSpeckle consistently produced higher-quality images.https://ieeexplore.ieee.org/document/10752631/Convolutional neural networkdeep learninghybrid networkimage denoisingintraoral ultrasoundspeckle noise |
| spellingShingle | Anparasy Sivaanpu Kumaradevan Punithakumar Rui Zheng Michelle Noga Dean Ta Edmond H. M. Lou Lawrence H. Le Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network IEEE Access Convolutional neural network deep learning hybrid network image denoising intraoral ultrasound speckle noise |
| title | Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network |
| title_full | Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network |
| title_fullStr | Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network |
| title_full_unstemmed | Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network |
| title_short | Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network |
| title_sort | speckle noise reduction for medical ultrasound images using hybrid cnn transformer network |
| topic | Convolutional neural network deep learning hybrid network image denoising intraoral ultrasound speckle noise |
| url | https://ieeexplore.ieee.org/document/10752631/ |
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