An efficient noisy pixels detection model for CT images using extreme learning machines
Yükleniyor...
Tarih
2018-06
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sveuciliste Josipa Jurja Strossmayera u Osijeku
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
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.
Açıklama
Anahtar Kelimeler
Detection, ELM, Filtering, Medical Imaging, MSE, PSNR
Kaynak
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
25
Sayı
3
Künye
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
