Image processing and algorithms for medical applications
Image degradation is a significant issue in medical imaging, as it may obscure the fine details of anatomical structures and organizations. This not only reduces the usability of the images thus increasing the time and monetary costs associated with diagnostic procedures, but also significantly impa...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1755992024-05-03T15:45:38Z Image processing and algorithms for medical applications Ma, Tianxing Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Engineering Medicine, Health and Life Sciences Medical image processing Denoise Image degradation is a significant issue in medical imaging, as it may obscure the fine details of anatomical structures and organizations. This not only reduces the usability of the images thus increasing the time and monetary costs associated with diagnostic procedures, but also significantly impact the accuracy of subsequent image analysis and interpretation. Noise and blur are are the two most common types of degradation. This project evaluates and compares the effectiveness of several image filtering methods, including Gaussian filter, Median filter, DWT filter, NLM filter, BM3D method, U-Net and Restormer model in restoring medical images with various levels and types of synthetic noise or blur to highlight the performance of deep learning based methods like U-Net and their great potential for application in medical image processing. Keywords: Medical image processing, Medical image denoising, Medical image deblurring, Medical image filtering, Deep learning, U-Net Master's degree 2024-04-30T08:15:01Z 2024-04-30T08:15:01Z 2024 Thesis-Master by Coursework Ma, T. (2024). Image processing and algorithms for medical applications. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175599 https://hdl.handle.net/10356/175599 en application/pdf Nanyang Technological University |
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Engineering Medicine, Health and Life Sciences Medical image processing Denoise |
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Engineering Medicine, Health and Life Sciences Medical image processing Denoise Ma, Tianxing Image processing and algorithms for medical applications |
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Image degradation is a significant issue in medical imaging, as it may obscure the fine details of anatomical structures and organizations. This not only reduces the usability of the images thus increasing the time and monetary costs associated with diagnostic procedures, but also significantly impact the accuracy of subsequent image analysis and interpretation. Noise and blur are are the two most common types of degradation. This project evaluates and compares the effectiveness of several image filtering methods, including Gaussian filter, Median filter, DWT filter, NLM filter, BM3D method, U-Net and Restormer model in restoring medical images with various levels and types of synthetic noise or blur to highlight the performance of deep learning based methods like U-Net and their great potential for application in medical image processing.
Keywords: Medical image processing, Medical image denoising, Medical image deblurring, Medical image filtering, Deep learning, U-Net |
author2 |
Mohammed Yakoob Siyal |
author_facet |
Mohammed Yakoob Siyal Ma, Tianxing |
format |
Thesis-Master by Coursework |
author |
Ma, Tianxing |
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Ma, Tianxing |
title |
Image processing and algorithms for medical applications |
title_short |
Image processing and algorithms for medical applications |
title_full |
Image processing and algorithms for medical applications |
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Image processing and algorithms for medical applications |
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Image processing and algorithms for medical applications |
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image processing and algorithms for medical applications |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/175599 |
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1800916418235990016 |