An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

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ÇALIŞKAN A., ÇEVİK U.

TEHNICKI VJESNIK-TECHNICAL GAZETTE, cilt.25, sa.3, ss.679-686, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.17559/tv-20171220221947
  • Dergi Adı: TEHNICKI VJESNIK-TECHNICAL GAZETTE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.679-686
  • Anahtar Kelimeler: detection, ELM, filtering, medical imaging, MSE, PSNR, TRANSFORM, ALGORITHM, NETWORKS
  • Çukurova Üniversitesi Adresli: Evet

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.