Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter

ABSTRACT

Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality.

KEYWORDS:

• Denoising
• discrete wavelet transform
• stationary wavelet transform
• bayesShrink
• Wiener filter

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No potential conflict of interest was reported by the author(s).

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Hilal Naimi

Hilal Naimi received his Licence diploma, Master, and Doctorat in Electronics, from university of Djelfa, Algeria. He is a Professor and member in Laboratoire de Recherche Modélisation Simulation et Optimisation des Systèmes Complexes Réels, at the University of Djelfa, Algeria. His research interests include digital signal processing, modern control systems, image processing and Biomedical Engineering.