Current Search: Tomography--Image quality (x)
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Title
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Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
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Creator
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Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
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Abstract/Description
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Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
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Date Issued
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2018
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PURL
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http://purl.flvc.org/fau/fd/FA00013115
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Subject Headings
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Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
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Format
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Document (PDF)
