Open Access
ARTICLE
Deep CNN Model for Multimodal Medical Image Denoising
Walid El-Shafai1,2, Amira A. Mahmoud1, Anas M. Ali1,3, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1
1 Department Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
2 Security Engineering Laboratory, Department of Computer Science, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Alexandria Higher Institute of Engineering & Technology (AIET), Alexandria, Egypt
4 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Amel A. Alhussan. Email:
Computers, Materials & Continua 2022, 73(2), 3795-3814. https://doi.org/10.32604/cmc.2022.029134
Received 25 February 2022; Accepted 30 March 2022; Issue published 16 June 2022
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 (
Cr), 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.
Keywords
Cite This Article
APA Style
El-Shafai, W., Mahmoud, A.A., Ali, A.M., El-Rabaie, E.M., Taha, T.E. et al. (2022). Deep CNN model for multimodal medical image denoising. Computers, Materials & Continua, 73(2), 3795-3814. https://doi.org/10.32604/cmc.2022.029134
Vancouver Style
El-Shafai W, Mahmoud AA, Ali AM, El-Rabaie EM, Taha TE, Zahran OF, et al. Deep CNN model for multimodal medical image denoising. Comput Mater Contin. 2022;73(2):3795-3814 https://doi.org/10.32604/cmc.2022.029134
IEEE Style
W. El-Shafai et al., "Deep CNN Model for Multimodal Medical Image Denoising," Comput. Mater. Contin., vol. 73, no. 2, pp. 3795-3814. 2022. https://doi.org/10.32604/cmc.2022.029134