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Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning

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Date Issued:
2018
Abstract/Description:
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 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.
Title: Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
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Name(s): Mohammadi Khoroushadi, Mohammad Sadegh, author
Leventouri, Theodora, Thesis advisor
Zhuang, Hanqi, Thesis advisor
Florida Atlantic University, Degree grantor
Charles E. Schmidt College of Science
Department of Physics
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2018
Date Issued: 2018
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 95 p.
Language(s): English
Abstract/Description: 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 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.
Identifier: FA00013115 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2018.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Tomography--Image quality
Wavelets (Mathematics)
Deep learning
Tomography, X-Ray Computed
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013115
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.