An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images
Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured pictur...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423002167 |
| _version_ | 1827815760539418624 |
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| author | R. Radhika Rashima Mahajan |
| author_facet | R. Radhika Rashima Mahajan |
| author_sort | R. Radhika |
| collection | DOAJ |
| description | Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods. |
| first_indexed | 2024-03-12T00:05:14Z |
| format | Article |
| id | doaj.art-781dc935805f4816a68d64e60fa5d299 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-12T00:05:14Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-781dc935805f4816a68d64e60fa5d2992023-09-17T04:57:28ZengElsevierMeasurement: Sensors2665-91742023-10-0129100880An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI imagesR. Radhika0Rashima Mahajan1Corresponding author.; Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, IndiaComputer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, IndiaMedical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.http://www.sciencedirect.com/science/article/pii/S2665917423002167MRI imageNoise suppressionFilteringAdaptive optimum weighted mean filterBilateral filterImpulsive noise and crow optimization algorithm |
| spellingShingle | R. Radhika Rashima Mahajan An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images Measurement: Sensors MRI image Noise suppression Filtering Adaptive optimum weighted mean filter Bilateral filter Impulsive noise and crow optimization algorithm |
| title | An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images |
| title_full | An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images |
| title_fullStr | An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images |
| title_full_unstemmed | An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images |
| title_short | An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images |
| title_sort | adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac mri images |
| topic | MRI image Noise suppression Filtering Adaptive optimum weighted mean filter Bilateral filter Impulsive noise and crow optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423002167 |
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