Original scientific paper

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

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.

Keywords

detection; ELM; filtering; medical imaging; MSE; PSNR

Hrčak ID:

202567

URI

https://hrcak.srce.hr/202567

Publication date:

28.6.2018.

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