Name
Abstract | ||
|---|---|---|
Image fusion can provide more extensive information since it combines two or more different images. Cloud model is a recently proposed theory in artificial intelligence and has the advantage of taking the randomness and fuzziness into account. In this paper, we introduce a novel multimodal medical image fusion method by cloud model theory. The proposed method fits the histograms of input images using the high-order spline function firstly and then divides intervals in line with the valley point of the fitted curve. On this basis, cloud models are generated adaptively through the reverse cloud generator. Finally, cloud reasoning rules are designed to achieve the fused image. Experimental results demonstrate that the fused images by proposed method show more image details and lesion regions than existing methods. The objective image quality assessment metrics on the fused images also show the superiority of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11760-017-1176-6 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Multimodal medical image, Image fusion, Artificial intelligence, Cloud model theory, Image histogram, Image quality assessment | Spline (mathematics),Computer vision,Histogram,Feature detection (computer vision),Pattern recognition,Image fusion,Computer science,Image quality,Artificial intelligence,Image histogram,Randomness,Cloud computing | Journal |
Volume | Issue | ISSN |
12 | 3 | 1863-1703 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weisheng Li | 1 | 141 | 29.73 |
| Jia Zhao | 2 | 36 | 8.80 |
| Bin Xiao | 3 | 7 | 5.18 |