@Article{cmc.2022.029134,
AUTHOR = {Walid El-Shafai, Amira A. Mahmoud, Anas M. Ali, El-Sayed M. El-Rabaie, Taha E. Taha, Osama F. Zahran, Adel S. El-Fishawy, Naglaa F. Soliman, Amel A. Alhussan, Fathi E. Abd El-Samie},
TITLE = {Deep CNN Model for Multimodal Medical Image Denoising},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {73},
YEAR = {2022},
NUMBER = {2},
PAGES = {3795--3814},
URL = {http://www.techscience.com/cmc/v73n2/48384},
ISSN = {1546-2226},
ABSTRACT = {In the literature, numerous techniques have been employed to decrease noise in medical image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). These techniques are organized into two main classes: the Multiple Image (MI) and the Single Image (SI) techniques. In the MI techniques, images usually obtained for the same area scanned from different points of view are used. A single image is used in the entire procedure in the SI techniques. SI denoising techniques can be carried out both in a transform or spatial domain. This paper is concerned with single-image noise reduction techniques because we deal with single medical images. The most well-known spatial domain noise reduction techniques, including Gaussian filter, Kuan filter, Frost filter, Lee filter, Gabor filter, Median filter, Homomorphic filter, Speckle reducing anisotropic diffusion (SRAD), Nonlocal-Means (NL-Means), and Total Variation (TV), are studied. Also, the transform domain noise reduction techniques, including wavelet-based and Curvelet-based techniques, and some hybridization techniques are investigated. Finally, a deep (Convolutional Neural Network) CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities. This model utilizes the Batch Normalization (BN) and the ReLU as a basic structure. As a result, it attained a considerable improvement over the traditional techniques. The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (<i>C</i><sub><i>r</i></sub>), and system complexity to determine the optimum denoising algorithm to be applied universally. Based on the quality metrics, it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditional denoising techniques.},
DOI = {10.32604/cmc.2022.029134}
}