SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images
Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Medicine in Novel Technology and Devices |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590093523000292 |
| _version_ | 1797790825384509440 |
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| author | Idowu Paul Okuwobi Zhixiang Ding Jifeng Wan Jiajia Jiang |
| author_facet | Idowu Paul Okuwobi Zhixiang Ding Jifeng Wan Jiajia Jiang |
| author_sort | Idowu Paul Okuwobi |
| collection | DOAJ |
| description | Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment. |
| first_indexed | 2024-03-13T02:10:02Z |
| format | Article |
| id | doaj.art-5dedea8f733340709c4bf75911215d83 |
| institution | Directory Open Access Journal |
| issn | 2590-0935 |
| language | English |
| last_indexed | 2024-03-13T02:10:02Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Medicine in Novel Technology and Devices |
| spelling | doaj.art-5dedea8f733340709c4bf75911215d832023-07-01T04:35:36ZengElsevierMedicine in Novel Technology and Devices2590-09352023-06-0118100234SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical imagesIdowu Paul Okuwobi0Zhixiang Ding1Jifeng Wan2Jiajia Jiang3School of Artificial Intelligence, Guilin University of Electronic Technology, #1 Jinji Road, Guilin, 541004, China; Corresponding author.Department of Ophthalmology, Affiliated Hospital of Guilin Medical University, 1# Zhiyuan Rd, Guilin, 541199, ChinaDepartment of Ophthalmology, Affiliated Hospital of Guilin Medical University, 1# Zhiyuan Rd, Guilin, 541199, ChinaDepartment of Ophthalmology, Affiliated Hospital of Guilin Medical University, 1# Zhiyuan Rd, Guilin, 541199, ChinaMedical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.http://www.sciencedirect.com/science/article/pii/S2590093523000292Computer tomographyMorphological reconstructionMedical imagesWavelets transform |
| spellingShingle | Idowu Paul Okuwobi Zhixiang Ding Jifeng Wan Jiajia Jiang SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images Medicine in Novel Technology and Devices Computer tomography Morphological reconstruction Medical images Wavelets transform |
| title | SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| title_full | SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| title_fullStr | SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| title_full_unstemmed | SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| title_short | SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| title_sort | swm de statistical wavelet model for joint denoising and enhancement for multimodal medical images |
| topic | Computer tomography Morphological reconstruction Medical images Wavelets transform |
| url | http://www.sciencedirect.com/science/article/pii/S2590093523000292 |
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