CNN-Based Medical Ultrasound Image Quality Assessment
The quality of ultrasound image is a key information in medical related application. It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. Yet, there is still no recognized quantitative standard about medical image quality assess...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9938367 |
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| _version_ | 1850109921328103424 |
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| author | Siyuan Zhang Yifan Wang Jiayao Jiang Jingxian Dong Weiwei Yi Wenguang Hou |
| author_facet | Siyuan Zhang Yifan Wang Jiayao Jiang Jingxian Dong Weiwei Yi Wenguang Hou |
| author_sort | Siyuan Zhang |
| collection | DOAJ |
| description | The quality of ultrasound image is a key information in medical related application. It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. Yet, there is still no recognized quantitative standard about medical image quality assessment (IQA) due to the fact that IQA is traditionally regarded as a subjective issue, especially in case of the ultrasound medical images. As such, the medical ultrasound IQA on basis of convolutional neural network (CNN) is quantitatively studied in this paper. Firstly, a dataset with 1063 ultrasound images is established through degenerating a certain number of original high-quality images. Subsequently, some operations are performed for the dataset including scoring and abnormal value screening. Then, 478 ultrasonic images are selected as the training and testing examples. The label of each example is obtained by averaging the scores of different doctors. Afterwards, a deep CNN network and a residuals network are taken to establish the IQA models. Meanwhile, the transfer learning strategy is introduced here to accelerate the training and improve the robustness of the model considering the fact that the ultrasound image samples are not abundant. At last, some tests are taken to evaluate the IQA models. They show that the CNN-based IQA is feasible and effective. |
| format | Article |
| id | doaj-art-433feccee63c4a2496d8ede4e02325ee |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-433feccee63c4a2496d8ede4e02325ee2025-08-20T02:37:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99383679938367CNN-Based Medical Ultrasound Image Quality AssessmentSiyuan Zhang0Yifan Wang1Jiayao Jiang2Jingxian Dong3Weiwei Yi4Wenguang Hou5College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe quality of ultrasound image is a key information in medical related application. It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. Yet, there is still no recognized quantitative standard about medical image quality assessment (IQA) due to the fact that IQA is traditionally regarded as a subjective issue, especially in case of the ultrasound medical images. As such, the medical ultrasound IQA on basis of convolutional neural network (CNN) is quantitatively studied in this paper. Firstly, a dataset with 1063 ultrasound images is established through degenerating a certain number of original high-quality images. Subsequently, some operations are performed for the dataset including scoring and abnormal value screening. Then, 478 ultrasonic images are selected as the training and testing examples. The label of each example is obtained by averaging the scores of different doctors. Afterwards, a deep CNN network and a residuals network are taken to establish the IQA models. Meanwhile, the transfer learning strategy is introduced here to accelerate the training and improve the robustness of the model considering the fact that the ultrasound image samples are not abundant. At last, some tests are taken to evaluate the IQA models. They show that the CNN-based IQA is feasible and effective.http://dx.doi.org/10.1155/2021/9938367 |
| spellingShingle | Siyuan Zhang Yifan Wang Jiayao Jiang Jingxian Dong Weiwei Yi Wenguang Hou CNN-Based Medical Ultrasound Image Quality Assessment Complexity |
| title | CNN-Based Medical Ultrasound Image Quality Assessment |
| title_full | CNN-Based Medical Ultrasound Image Quality Assessment |
| title_fullStr | CNN-Based Medical Ultrasound Image Quality Assessment |
| title_full_unstemmed | CNN-Based Medical Ultrasound Image Quality Assessment |
| title_short | CNN-Based Medical Ultrasound Image Quality Assessment |
| title_sort | cnn based medical ultrasound image quality assessment |
| url | http://dx.doi.org/10.1155/2021/9938367 |
| work_keys_str_mv | AT siyuanzhang cnnbasedmedicalultrasoundimagequalityassessment AT yifanwang cnnbasedmedicalultrasoundimagequalityassessment AT jiayaojiang cnnbasedmedicalultrasoundimagequalityassessment AT jingxiandong cnnbasedmedicalultrasoundimagequalityassessment AT weiweiyi cnnbasedmedicalultrasoundimagequalityassessment AT wenguanghou cnnbasedmedicalultrasoundimagequalityassessment |