Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images
Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to ex...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Astronomy |
| Online Access: | http://dx.doi.org/10.1155/2019/2397536 |
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| author | M. Zhu W. Liu B. Y. Wang M. F. Zhang W. W. Tian X. C. Yu T. H. Liang D. Wu D. Hu F. Q. Duan |
| author_facet | M. Zhu W. Liu B. Y. Wang M. F. Zhang W. W. Tian X. C. Yu T. H. Liang D. Wu D. Hu F. Q. Duan |
| author_sort | M. Zhu |
| collection | DOAJ |
| description | Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR). |
| format | Article |
| id | doaj-art-ae14a4e7d7a64a4e8236ed0c5745146f |
| institution | OA Journals |
| issn | 1687-7969 1687-7977 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Astronomy |
| spelling | doaj-art-ae14a4e7d7a64a4e8236ed0c5745146f2025-08-20T02:23:28ZengWileyAdvances in Astronomy1687-79691687-79772019-01-01201910.1155/2019/23975362397536Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical ImagesM. Zhu0W. Liu1B. Y. Wang2M. F. Zhang3W. W. Tian4X. C. Yu5T. H. Liang6D. Wu7D. Hu8F. Q. Duan9College of Information Science and Technology, Beijing Normal University, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaKey Laboratory of Optical Astronomy, National Astronomical Observatory of China, Beijing, ChinaThe University of Chinese Academy of Sciences, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaKey Laboratory of Optical Astronomy, National Astronomical Observatory of China, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaFilaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR).http://dx.doi.org/10.1155/2019/2397536 |
| spellingShingle | M. Zhu W. Liu B. Y. Wang M. F. Zhang W. W. Tian X. C. Yu T. H. Liang D. Wu D. Hu F. Q. Duan Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images Advances in Astronomy |
| title | Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images |
| title_full | Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images |
| title_fullStr | Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images |
| title_full_unstemmed | Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images |
| title_short | Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images |
| title_sort | extracting filaments based on morphology components analysis from radio astronomical images |
| url | http://dx.doi.org/10.1155/2019/2397536 |
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