Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images
Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance...
| Główni autorzy: | , , , , , , , |
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| Format: | Artykuł |
| Język: | English |
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MDPI AG
2022-07-01
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| Seria: | Sensors |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/1424-8220/22/15/5666 |
| _version_ | 1827601606422560768 |
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| author | Yiwen Liu Tao Wen Wei Sun Zhenyu Liu Xiaoying Song Xuan He Shuo Zhang Zhenning Wu |
| author_facet | Yiwen Liu Tao Wen Wei Sun Zhenyu Liu Xiaoying Song Xuan He Shuo Zhang Zhenning Wu |
| author_sort | Yiwen Liu |
| collection | DOAJ |
| description | Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled ‘black-box’ by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity. |
| first_indexed | 2024-03-09T04:59:54Z |
| format | Article |
| id | doaj.art-47c8d2a2e7014a5ab7ec4e01e9f885b5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:54Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-47c8d2a2e7014a5ab7ec4e01e9f885b52023-12-03T13:00:54ZengMDPI AGSensors1424-82202022-07-012215566610.3390/s22155666Graph-Based Motion Artifacts Detection Method from Head Computed Tomography ImagesYiwen Liu0Tao Wen1Wei Sun2Zhenyu Liu3Xiaoying Song4Xuan He5Shuo Zhang6Zhenning Wu7School of Computer Science and Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science, Neusoft Institute Guangdong, Foshan 528225, ChinaDepartment of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, ChinaDepartment of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaComputed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled ‘black-box’ by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.https://www.mdpi.com/1424-8220/22/15/5666complex networkscomputed tomography imagesmotion artifacts detectionnetwork topological characteristicsclassification |
| spellingShingle | Yiwen Liu Tao Wen Wei Sun Zhenyu Liu Xiaoying Song Xuan He Shuo Zhang Zhenning Wu Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images Sensors complex networks computed tomography images motion artifacts detection network topological characteristics classification |
| title | Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images |
| title_full | Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images |
| title_fullStr | Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images |
| title_full_unstemmed | Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images |
| title_short | Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images |
| title_sort | graph based motion artifacts detection method from head computed tomography images |
| topic | complex networks computed tomography images motion artifacts detection network topological characteristics classification |
| url | https://www.mdpi.com/1424-8220/22/15/5666 |
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