Real and Complex Valued Ripplet-I Transform for Medical Image Denoising and Analysis of Thresholding Constants and Scales Effects


  • Hüseyin Yaşar Ministry of Health of the Republic of Turkey
  • Murat Ceylan Department of Electrical and Electronics Engineering, Selcuk University, Konya, Turkey



Real and complex valued ripplet-I transform, Medical image denoising, Thresholding constant, Peak signal-to-noise ratio (PSNR), Mean structural similarity index (MSSIM), Feature similarity index (FSIM)


Medical image processing is an important diagnostic tool in the field of medical. Medical images might be affected by the noises that manipulate the resolution negatively during screening or transmission. These images need to be eliminated so as not to affect the diagnosis success negatively. In medical image denoising studies, using the multi-resolution analysis coefficients is a widely appreciated method. This study tested the success rate of real and complex valued ripplet-I transform for medical image denoising. Thanks to this study, the complex version of the newly suggested ripplet-I transform whose real version was used formerly in various studies was used in a medical image denoising application the first time. In the study tested with 40 liver images, 40 retinal images and 322 mammographic images, peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM) and feature similarity index (FSIM) were utilized to compare the successes of image denoising. In the wake of study, it was seen that the complex valued ripplet-I (CVR-I) transform gave better results than the real valued ripplet-I (RVR-I) transform when used in the same image denoising algorithm. This study also examined the effects that the changes in scale and thresholding constant values have on the medical image denoising results, thus making this study appear as a guideline.


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How to Cite

Yaşar, H., & Ceylan, M. (2017). Real and Complex Valued Ripplet-I Transform for Medical Image Denoising and Analysis of Thresholding Constants and Scales Effects. British Journal of Healthcare and Medical Research, 4(2), 10.