Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval
Magnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.829040/full |
| _version_ | 1831683572685078528 |
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| author | Guohua Zhou Guohua Zhou Guohua Zhou Bing Lu Xuelong Hu Tongguang Ni |
| author_facet | Guohua Zhou Guohua Zhou Guohua Zhou Bing Lu Xuelong Hu Tongguang Ni |
| author_sort | Guohua Zhou |
| collection | DOAJ |
| description | Magnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images from a huge database is of great significance to doctors’ auxiliary diagnosis and treatment. In this paper, combining the advantages of sparse representation and metric learning, a sparse representation-based discriminative metric learning (SRDML) approach is proposed for medical image retrieval of brain MRI. The SRDML approach uses a sparse representation framework to learn robust feature representation of brain MRI, and uses metric learning to project new features into the metric space with matching discrimination. In such a metric space, the optimal similarity measure is obtained by using the local constraints of atoms and the pairwise constraints of coding coefficients, so that the distance between similar images is less than the given threshold, and the distance between dissimilar images is greater than another given threshold. The experiments are designed and tested on the brain MRI dataset created by Chang. Experimental results show that the SRDML approach can obtain satisfactory retrieval performance and achieve accurate brain MRI image retrieval. |
| first_indexed | 2024-12-20T06:40:56Z |
| format | Article |
| id | doaj.art-9dc94a5df19a47bab028964f4d43597f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-20T06:40:56Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-9dc94a5df19a47bab028964f4d43597f2022-12-21T19:49:52ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-01-011510.3389/fnins.2021.829040829040Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image RetrievalGuohua Zhou0Guohua Zhou1Guohua Zhou2Bing Lu3Xuelong Hu4Tongguang Ni5School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou, ChinaSchool of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, ChinaMagnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images from a huge database is of great significance to doctors’ auxiliary diagnosis and treatment. In this paper, combining the advantages of sparse representation and metric learning, a sparse representation-based discriminative metric learning (SRDML) approach is proposed for medical image retrieval of brain MRI. The SRDML approach uses a sparse representation framework to learn robust feature representation of brain MRI, and uses metric learning to project new features into the metric space with matching discrimination. In such a metric space, the optimal similarity measure is obtained by using the local constraints of atoms and the pairwise constraints of coding coefficients, so that the distance between similar images is less than the given threshold, and the distance between dissimilar images is greater than another given threshold. The experiments are designed and tested on the brain MRI dataset created by Chang. Experimental results show that the SRDML approach can obtain satisfactory retrieval performance and achieve accurate brain MRI image retrieval.https://www.frontiersin.org/articles/10.3389/fnins.2021.829040/fullmedical image retrievalmagnetic resonance imagingbrain imagessparse representationmetric learning |
| spellingShingle | Guohua Zhou Guohua Zhou Guohua Zhou Bing Lu Xuelong Hu Tongguang Ni Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval Frontiers in Neuroscience medical image retrieval magnetic resonance imaging brain images sparse representation metric learning |
| title | Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval |
| title_full | Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval |
| title_fullStr | Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval |
| title_full_unstemmed | Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval |
| title_short | Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval |
| title_sort | sparse representation based discriminative metric learning for brain mri image retrieval |
| topic | medical image retrieval magnetic resonance imaging brain images sparse representation metric learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.829040/full |
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