An efficient noisy pixels detection model for CT images using extreme learning machines
| dc.authorid | 0000-0001-5039-6400 | en_US |
| dc.authorid | 0000-0002-0956-9725 | en_US |
| dc.contributor.author | Çalışkan, Abidin | |
| dc.contributor.author | Çevik, Ulus | |
| dc.date.accessioned | 2019-06-26T11:43:27Z | |
| dc.date.available | 2019-06-26T11:43:27Z | |
| dc.date.issued | 2018-06 | en_US |
| dc.department | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method. | en_US |
| dc.identifier.citation | Çalışkan, A., Çevik, U. (2018). An efficient noisy pixels detection model for CT images using extreme learning machines. Tehnicki Vjesnik 25(3), pp. 679-686. https://doi.org/10.17559/tv-20171220221947 | en_US |
| dc.identifier.endpage | 686 | en_US |
| dc.identifier.issn | 1330-3651 | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 679 | en_US |
| dc.identifier.uri | https://doi.org/10.17559/tv-20171220221947 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12402/2132 | |
| dc.identifier.volume | 25 | en_US |
| dc.identifier.wosquality | Q4 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Sveuciliste Josipa Jurja Strossmayera u Osijeku | en_US |
| dc.relation.isversionof | 10.17559/tv-20171220221947 | en_US |
| dc.relation.journal | Tehnicki Vjesnik | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Detection | en_US |
| dc.subject | ELM | en_US |
| dc.subject | Filtering | en_US |
| dc.subject | Medical Imaging | en_US |
| dc.subject | MSE | en_US |
| dc.subject | PSNR | en_US |
| dc.title | An efficient noisy pixels detection model for CT images using extreme learning machines | en_US |
| dc.type | Article | en_US |
