Effects of total variation regularization noise reduction algorithm in improved K-edge log-subtraction X-ray images with photon-counting cadmium telluride detectors
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim K. | - |
dc.contributor.author | Lee Y. | - |
dc.date.available | 2020-04-06T07:37:08Z | - |
dc.date.created | 2020-04-02 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 0030-4026 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/26391 | - |
dc.description.abstract | X-ray systems with photon-counting cadmium telluride (CdTe) detectors can achieve greatly improved images by using the K-edge log-subtraction (KELS) imaging method. This paper discusses methods for acquiring KELS images with photon-counting CdTe detectors and applying the modeled total variation (TV) regularization noise reduction algorithm to the obtained images. Monte Carlo simulation using the Geant4 Application for Tomographic Emission platform was used to model the system. To demonstrate the usefulness of the proposed TV algorithm, we investigated the normalized noise power spectrum, contrast to noise ratio, and no reference-based assessment parameter using a natural image quality evaluator. The results demonstrate that the proposed TV noise reduction regularization algorithm can better preserve image details than the conventional denoising methods in all the evaluation parameters. © 2020 Elsevier GmbH | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier GmbH | - |
dc.relation.isPartOf | Optik | - |
dc.title | Effects of total variation regularization noise reduction algorithm in improved K-edge log-subtraction X-ray images with photon-counting cadmium telluride detectors | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000522772400176 | - |
dc.identifier.doi | 10.1016/j.ijleo.2020.164380 | - |
dc.identifier.bibliographicCitation | Optik, v.206 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85079318670 | - |
dc.citation.title | Optik | - |
dc.citation.volume | 206 | - |
dc.contributor.affiliatedAuthor | Lee Y. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Cadmium telluride | - |
dc.subject.keywordAuthor | K-edge log-subtraction imaging method | - |
dc.subject.keywordAuthor | Monte Carlo simulation | - |
dc.subject.keywordAuthor | Photon counting detector | - |
dc.subject.keywordAuthor | Total variation noise reduction approach | - |
dc.subject.keywordPlus | Cadmium telluride | - |
dc.subject.keywordPlus | II-VI semiconductors | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Image quality | - |
dc.subject.keywordPlus | Intelligent systems | - |
dc.subject.keywordPlus | Monte Carlo methods | - |
dc.subject.keywordPlus | Noise abatement | - |
dc.subject.keywordPlus | Parameter estimation | - |
dc.subject.keywordPlus | Photons | - |
dc.subject.keywordPlus | Quality control | - |
dc.subject.keywordPlus | Simulation platform | - |
dc.subject.keywordPlus | X ray detectors | - |
dc.subject.keywordPlus | Cadmium telluride detectors | - |
dc.subject.keywordPlus | Geant4 application for tomographic emissions | - |
dc.subject.keywordPlus | Noise reduction algorithms | - |
dc.subject.keywordPlus | Normalized noise power spectrum | - |
dc.subject.keywordPlus | Photon counting detectors | - |
dc.subject.keywordPlus | Subtraction imaging | - |
dc.subject.keywordPlus | Total variation | - |
dc.subject.keywordPlus | Total variation regularization | - |
dc.subject.keywordPlus | Image denoising | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.