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Izvorni znanstveni članak
https://doi.org/10.17559/TV-20171220221947
An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines
Abidin Çalişkan
orcid.org/0000-0001-5039-6400
; Department of Computer Engineering, Batman University, Batman, Turkey
Ulus Çevik
; Department of Electrical and Electronics Engineering, Çukurova University, Adana, Turkey
| Puni tekst: engleski, pdf (3 MB) | str. 679-686 | citiraj | |
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APA 6th Edition Çalişkan, A. i Çevik, U. (2018). An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines. Tehnički vjesnik, 25 (3), 679-686. https://doi.org/10.17559/TV-20171220221947 MLA 8th Edition Çalişkan, Abidin i Ulus Çevik. "An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines." Tehnički vjesnik, vol. 25, br. 3, 2018, str. 679-686. https://doi.org/10.17559/TV-20171220221947. Citirano 14.12.2019. Chicago 17th Edition Çalişkan, Abidin i Ulus Çevik. "An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines." Tehnički vjesnik 25, br. 3 (2018): 679-686. https://doi.org/10.17559/TV-20171220221947 Harvard Çalişkan, A., i Çevik, U. (2018). 'An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines', Tehnički vjesnik, 25(3), str. 679-686. https://doi.org/10.17559/TV-20171220221947 Vancouver Çalişkan A, Çevik U. An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines. Tehnički vjesnik [Internet]. 2018 [pristupljeno 14.12.2019.];25(3):679-686. https://doi.org/10.17559/TV-20171220221947 IEEE A. Çalişkan i U. Çevik, "An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines", Tehnički vjesnik, vol.25, br. 3, str. 679-686, 2018. [Online]. https://doi.org/10.17559/TV-20171220221947 |
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Sažetak
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.
Ključne riječi
detection; ELM; filtering; medical imaging; MSE; PSNR
Hrčak ID: 202567
URI
https://hrcak.srce.hr/202567
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