An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/21/12069 |
| _version_ | 1797632165184274432 |
|---|---|
| author | Teresa Kwamboka Abuya Richard Maina Rimiru George Onyango Okeyo |
| author_facet | Teresa Kwamboka Abuya Richard Maina Rimiru George Onyango Okeyo |
| author_sort | Teresa Kwamboka Abuya |
| collection | DOAJ |
| description | Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation. |
| first_indexed | 2024-03-11T11:33:02Z |
| format | Article |
| id | doaj.art-d1c32c36ba6b41cd8293b34335ecb223 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:33:02Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d1c32c36ba6b41cd8293b34335ecb2232023-11-10T14:59:48ZengMDPI AGApplied Sciences2076-34172023-11-0113211206910.3390/app132112069An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT ImagesTeresa Kwamboka Abuya0Richard Maina Rimiru1George Onyango Okeyo2Department of Computer Science, Jomo Kenyatta University of Agriculture & Technology, Nairobi P.O. Box 62000-00200, KenyaCollege of Engineering, Carnegie Mellon University, Kigali BP 6150, RwandaCollege of Engineering, Carnegie Mellon University, Kigali BP 6150, RwandaDenoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.https://www.mdpi.com/2076-3417/13/21/12069denoising CNNimage denoisingadditive Gaussian blur noiseCT imageswavelet transformanisotropic Gaussian filter |
| spellingShingle | Teresa Kwamboka Abuya Richard Maina Rimiru George Onyango Okeyo An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images Applied Sciences denoising CNN image denoising additive Gaussian blur noise CT images wavelet transform anisotropic Gaussian filter |
| title | An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images |
| title_full | An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images |
| title_fullStr | An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images |
| title_full_unstemmed | An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images |
| title_short | An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images |
| title_sort | image denoising technique using wavelet anisotropic gaussian filter based denoising convolutional neural network for ct images |
| topic | denoising CNN image denoising additive Gaussian blur noise CT images wavelet transform anisotropic Gaussian filter |
| url | https://www.mdpi.com/2076-3417/13/21/12069 |
| work_keys_str_mv | AT teresakwambokaabuya animagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages AT richardmainarimiru animagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages AT georgeonyangookeyo animagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages AT teresakwambokaabuya imagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages AT richardmainarimiru imagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages AT georgeonyangookeyo imagedenoisingtechniqueusingwaveletanisotropicgaussianfilterbaseddenoisingconvolutionalneuralnetworkforctimages |