Improving brain MRI denoising using convolutional AutoEncoder and sparse representations

Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is...

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Bibliographic Details
Published in:Expert systems with applications Vol. 263; p. 125711
Main Authors: Velayudham, A, Madhan Kumar, K., Krishna Priya, MS
Format: Journal Article
Language:English
Published: Elsevier Ltd 05.03.2025
Subjects:
Autoencoder
Crossover Boosted Elephant Herd Optimization
Deep learning
Denoising
MRI
Neural Network
Noise
Deep learning
Neural Network
Noise
Autoencoder
MRI
Crossover Boosted Elephant Herd Optimization
Denoising
ISSN:0957-4174
Online Access:Get full text
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Summary:Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is proposed for brain MRI image denoising. In this model, the encoder block extracts relevant features from the image while reducing overfitting, and the OMP algorithm creates a sparse representation to enhance denoising. To improve performance and computational efficiency, the traditional greedy search process in OMP is replaced with the Crossover Boosted Elephant Herd Optimization (CBEHO) algorithm. Rather than searching for atoms, CBEHO optimizes parameter selection, thereby reducing search times and enhancing convergence in the OMP process. Using this optimized sparse representation, the model iteratively improves the original image’s approximation by updating residuals and the support set. The decoder block then reconstructs the denoised image features. The proposed method was tested on multiple datasets, including the RSNA MICCAI PNG dataset, the Brain Tumor Detection MRI (BTD-MRI) dataset, the Brain Tumor Classification MRI (BTC-MRI) Images dataset, and the Brain Tumor Segmentation (BraTS2020) dataset. The results show that the CAE-OMP model achieves Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) values of 0.989 and 47.345 on the BTD-MRI dataset, 0.985 and 46.321 on the RSNA MICCAI PNG dataset, 0.978 and 45.453 on the BTC-MRI dataset, and 0.981 and 46.892 on the BraTS2020 dataset, all evaluated at a 15% noise level. These outcomes indicate that the proposed CAE-OMP model outperforms existing methods, demonstrating superior efficiency for denoising brain MRI images.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.125711