Medical Image Fusion Based on Feature Extraction and Sparse Representation
As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mec...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2017/3020461 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849415358723653632 |
|---|---|
| author | Yin Fei Gao Wei Song Zongxi |
| author_facet | Yin Fei Gao Wei Song Zongxi |
| author_sort | Yin Fei |
| collection | DOAJ |
| description | As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. |
| format | Article |
| id | doaj-art-56d42b5bef3347bb84e6eae09c925e41 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-56d42b5bef3347bb84e6eae09c925e412025-08-20T03:33:34ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/30204613020461Medical Image Fusion Based on Feature Extraction and Sparse RepresentationYin Fei0Gao Wei1Song Zongxi2Xi’an Institute of Optics and Precision Mechanics, Chinese Academic of Sciences, Xi’an 710119, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaAs a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.http://dx.doi.org/10.1155/2017/3020461 |
| spellingShingle | Yin Fei Gao Wei Song Zongxi Medical Image Fusion Based on Feature Extraction and Sparse Representation International Journal of Biomedical Imaging |
| title | Medical Image Fusion Based on Feature Extraction and Sparse Representation |
| title_full | Medical Image Fusion Based on Feature Extraction and Sparse Representation |
| title_fullStr | Medical Image Fusion Based on Feature Extraction and Sparse Representation |
| title_full_unstemmed | Medical Image Fusion Based on Feature Extraction and Sparse Representation |
| title_short | Medical Image Fusion Based on Feature Extraction and Sparse Representation |
| title_sort | medical image fusion based on feature extraction and sparse representation |
| url | http://dx.doi.org/10.1155/2017/3020461 |
| work_keys_str_mv | AT yinfei medicalimagefusionbasedonfeatureextractionandsparserepresentation AT gaowei medicalimagefusionbasedonfeatureextractionandsparserepresentation AT songzongxi medicalimagefusionbasedonfeatureextractionandsparserepresentation |