Removal of speckle noises from ultrasound images using five different deep learning networks

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dc.contributor.authorKaraoglu, Onur
dc.contributor.authorBilge, Hasan Sakir
dc.contributor.authorUluer, Ihsan
dc.date.accessioned2024-09-29T15:57:35Z
dc.date.available2024-09-29T15:57:35Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractImage enhancement methods are applied to medical images to reduce the noise that they contain. There are many academic studies in the literature using classical image enhancement methods. Ultrasound imaging is a medical imaging method that is used for the diagnosis of diseases. In this study, speckle noises with Rayleigh distribution at four different noise levels (sigma = 0.10, 0.25, 0.50, 0.75) are added to ultrasound images of the brachial plexus nerve region. Five different deep learning networks (Dilated Convolution Autoencoder Denoising Network/Di-Conv-AE-Net, Denoising U-Shaped Net/D-U-Net, BatchRenormalization U-Net/Br-U-Net, Generative Adversarial Denoising Network/DGan-Net, and CNN Residual Network/DeRNet) are used for reducing the speckle noises of the ultrasound images. The performances of the deep networks are compared with block-matching and 3D filtering (BM3D), which is one of the most preferred classical image enhancement algorithms; with classical filters including Bilateral, Frost, Kuan, Lee, Mean, and Median Filters; and with deep learning networks including Learning Pixel Distribution Prior with Wider Convolution for Image Denoising (WIN5-RB), Denoising Prior Driven Deep Neural Network for Image Restoration (DPDNN), and Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks (FPD-M-Net). Network performance is evaluated according to peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and runtime criteria and the proposed deep learning networks are shown to outperform the other networks. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.en_US
dc.identifier.doi10.1016/j.jestch.2021.06.010
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-85109763365en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2021.06.010
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4908
dc.identifier.volume29en_US
dc.identifier.wosWOS:000807496500008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Science and Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectUltrasound imagingen_US
dc.subjectDeep learningen_US
dc.subjectSpeckle noiseen_US
dc.subjectDenoisingen_US
dc.subjectImage enhancementen_US
dc.titleRemoval of speckle noises from ultrasound images using five different deep learning networksen_US
dc.typeArticleen_US

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