| Title |
Numerical analysis of SLSSIM similarity on medical X-ray image domain / |
| Authors |
Brusokas, Jonas ; Petkevičius, Linas |
| Full Text |
|
| Is Part of |
IVUS 2019. Proceedings of the International Conference on Information Technologies, Kaunas, Lithuania, April 25, 2019 / Edited by Robertas Damaševičius, Tomas Krilavičius, Audrius Lopata, Dawid Połap, Marcin Woźniak.. Aachen : CEUR-WS.org. 2019, p. 91-97 |
| Keywords [eng] |
image similarity metrics ; low dose X-ray imaging ; medical X-ray images |
| Abstract [eng] |
The X-ray has been adopted and used for various purposes including medical diagnostics. To remove noise created by new low dose X-ray imaging procedures and reduce medical image size, X-ray image reconstruction and lossless compression using deep neural networks are being researched. To enable this, image similarity metrics capable of performing well on X-ray images must be used. In this paper, the requirements for medical X-ray similarity metrics are defined. A new similarity metric is proposed taking into account the quality of structures within different intensity levels. An analysis is given comparing the proposed and other currently known metrics performance on real X-ray images in simulated scenarios. |
| Published |
Aachen : CEUR-WS.org |
| Type |
Conference paper |
| Language |
Lithuanian |
| Publication date |
2019 |
| CC license |
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