Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising

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dc.contributor.authorCeylan, Murat
dc.contributor.authorCanbilen, Ayşe Elif
dc.date.accessioned2020-03-26T19:32:34Z
dc.date.available2020-03-26T19:32:34Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractNoise reduces the quality of medical images and raise the difficulties of diagnosis. Although the wavelet transform has already been used in medical noise removal applications extensively, there are many other multi-resolution analysis methods proposed in recent years for denoising. The main goal of this study is comparing the image denoising abilities of some of these methods with wavelet transform. In this paper, image denoising is implemented by a three-stage methodology. Effectiveness of the multiresolution analysis methodologies has been investigated for standard test images beside magnetic resonans, mammography and fundus images. Performances of the transforms are compared by using peak signal to noise ratio, mean square error, mean structural similarity index and feature similarity index. The best results are obtained by tetrolet transform for random and rician noiseswith the benchmark images. Medical image denoising performance of Tetrolet transform is compared to other multiresolution analysis methods for the first time in the literature with this study. It surpassed ridgelet and haar wavelet transforms while the noise ratio was low. On the other hand, it is seen that curvelet transforms are effectively produce the best results for all rates of noise on medical imagesen_US
dc.identifier.endpage231en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue4en_US
dc.identifier.startpage222en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TWpjeE9EY3hNUT09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34495
dc.identifier.volume5en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.titlePerformance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoisingen_US
dc.typeArticleen_US

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