A novel dictionary learning-based approach for Ultrasound Elastography denoising
Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022537?viewType=HTML |
| _version_ | 1817999849970204672 |
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| author | Yihua Song Chen Ge Ningning Song Meili Deng |
| author_facet | Yihua Song Chen Ge Ningning Song Meili Deng |
| author_sort | Yihua Song |
| collection | DOAJ |
| description | Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis. |
| first_indexed | 2024-04-14T03:14:36Z |
| format | Article |
| id | doaj.art-3e85c515c2704129841d4ad6a7ef2429 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-14T03:14:36Z |
| publishDate | 2022-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-3e85c515c2704129841d4ad6a7ef24292022-12-22T02:15:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-08-011911115331154310.3934/mbe.2022537A novel dictionary learning-based approach for Ultrasound Elastography denoisingYihua Song 0Chen Ge1Ningning Song2Meili Deng31. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China2. Shandong Vocational and Technical University of Engineering, Jinan 250200, China3. Nanjing First Hospital, Nanjing 210000, China4. China United Network Communications Corporation, Nanjing 210000, ChinaUltrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis.https://www.aimspress.com/article/doi/10.3934/mbe.2022537?viewType=HTMLdictionary learningk-svddenoisingpcaultrasound elastography |
| spellingShingle | Yihua Song Chen Ge Ningning Song Meili Deng A novel dictionary learning-based approach for Ultrasound Elastography denoising Mathematical Biosciences and Engineering dictionary learning k-svd denoising pca ultrasound elastography |
| title | A novel dictionary learning-based approach for Ultrasound Elastography denoising |
| title_full | A novel dictionary learning-based approach for Ultrasound Elastography denoising |
| title_fullStr | A novel dictionary learning-based approach for Ultrasound Elastography denoising |
| title_full_unstemmed | A novel dictionary learning-based approach for Ultrasound Elastography denoising |
| title_short | A novel dictionary learning-based approach for Ultrasound Elastography denoising |
| title_sort | novel dictionary learning based approach for ultrasound elastography denoising |
| topic | dictionary learning k-svd denoising pca ultrasound elastography |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022537?viewType=HTML |
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