DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Eunhee | ko |
dc.contributor.author | Chang, Won | ko |
dc.contributor.author | Yoo, Jaejun | ko |
dc.contributor.author | Ye, Jong Chul | ko |
dc.date.accessioned | 2018-07-24T01:38:14Z | - |
dc.date.available | 2018-07-24T01:38:14Z | - |
dc.date.created | 2018-06-25 | - |
dc.date.created | 2018-06-25 | - |
dc.date.created | 2018-06-25 | - |
dc.date.created | 2018-06-25 | - |
dc.date.issued | 2018-06 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON MEDICAL IMAGING, v.37, no.6, pp.1358 - 1369 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | http://hdl.handle.net/10203/243703 | - |
dc.description.abstract | Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | X-RAY CT | - |
dc.subject | TOTAL-VARIATION MINIMIZATION | - |
dc.subject | IMAGE-RECONSTRUCTION | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | ALGORITHM | - |
dc.subject | MATRIX | - |
dc.subject | SPARSE | - |
dc.title | Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network | - |
dc.type | Article | - |
dc.identifier.wosid | 000434302700007 | - |
dc.identifier.scopusid | 2-s2.0-85045190583 | - |
dc.type.rims | ART | - |
dc.citation.volume | 37 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 1358 | - |
dc.citation.endingpage | 1369 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.identifier.doi | 10.1109/TMI.2018.2823756 | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.contributor.nonIdAuthor | Chang, Won | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | low-dose CT | - |
dc.subject.keywordAuthor | framelet denoising | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | convolution framelets | - |
dc.subject.keywordPlus | X-RAY CT | - |
dc.subject.keywordPlus | TOTAL-VARIATION MINIMIZATION | - |
dc.subject.keywordPlus | IMAGE-RECONSTRUCTION | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | MATRIX | - |
dc.subject.keywordPlus | SPARSE | - |
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