Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering
文献类型：期刊论文
| 作者 | Hu, Qiu1,2; Hu, Shaohai1,2; Zhang, Fengzhen3 |
| 刊名 | SIGNAL PROCESSING-IMAGE COMMUNICATION
 |
| 出版日期 | 2020-04-01 |
| 卷号 | 83页码:10 |
| 关键词 | Image fusion Multi-modality medical image Sparse representation Gabor filter Non-subsampled contourlet transform |
| ISSN号 | 0923-5965 |
| DOI | 10.1016/j.image.2019.115758 |
| 英文摘要 | Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment. |
| WOS关键词 | TRANSFORM ; REPRESENTATIONS |
| 资助项目 | Natural Science Foundation of China[61572063] ; Youth Science Foundation Project of China[61902371] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000524378500011 |
| 出版者 | ELSEVIER |
| 资助机构 | Natural Science Foundation of China ; Natural Science Foundation of China ; Youth Science Foundation Project of China ; Youth Science Foundation Project of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Youth Science Foundation Project of China ; Youth Science Foundation Project of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Youth Science Foundation Project of China ; Youth Science Foundation Project of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Youth Science Foundation Project of China ; Youth Science Foundation Project of China |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/55975]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Hu, Shaohai |
| 作者单位 | 1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 2.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Qiu,Hu, Shaohai,Zhang, Fengzhen. Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering[J]. SIGNAL PROCESSING-IMAGE COMMUNICATION,2020,83:10. |
| APA | Hu, Qiu,Hu, Shaohai,&Zhang, Fengzhen.(2020).Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering.SIGNAL PROCESSING-IMAGE COMMUNICATION,83,10. |
| MLA | Hu, Qiu,et al."Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering".SIGNAL PROCESSING-IMAGE COMMUNICATION 83(2020):10. |
入库方式： OAI收割
来源：国家天文台
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