Name
Abstract | ||
|---|---|---|
This paper presents a Detector of Structural Similarity (DSS) to minimize the visual differences between brightfield and confocal microscopic images. The context of this work is that it is very challenging to effectively register such images due to a low structural similarity in image contents. To address this issue, DSS aims to maximize the structural similarity by utilizing the intensity relationships among red-green-blue (RGB) channels in images. Technically, DSS can be combined with any multi-modal image registration technique in registering brightfield and confocal microscopic images. Our experimental results show that DSS significantly increases the visual similarity in such images, thereby improving the registration performance of an existing state-of-the-art multi-modal image registration technique by up to approximately 27%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11042-017-4669-y | Multimedia Tools and Applications |
Keywords | Field | DocType |
Multi-modal microscopic images, Structural similarity, Image registration | Computer vision,Pattern recognition,Color histogram,Computer science,Image matching,Binary image,Structural similarity,Artificial intelligence,Confocal,Image registration,Channel (digital image),Color image | Conference |
Volume | Issue | ISSN |
77 | 6 | 1573-7721 |
Citations | PageRank | References |
2 | 0.41 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohua Lv | 1 | 21 | 3.86 |
| Shyh Wei Teng | 2 | 151 | 21.02 |
| Guojun Lu | 3 | 1965 | 82.04 |
| Martin Lackmann | 4 | 22 | 3.09 |