Deep Perceptual Enhancement for Medical Image Analysis
DC Field | Value | Language |
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dc.contributor.author | Sharif, S.M.A. | - |
dc.contributor.author | Naqvi, R.A. | - |
dc.contributor.author | Biswas, M. | - |
dc.contributor.author | Loh, Woong-Kee | - |
dc.date.accessioned | 2022-10-17T02:40:12Z | - |
dc.date.available | 2022-10-17T02:40:12Z | - |
dc.date.created | 2022-05-25 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85703 | - |
dc.description.abstract | Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to- end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks performance and reveal the potentiality of such an enhancement method in real-world applications. IEEE | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Journal of Biomedical and Health Informatics | - |
dc.title | Deep Perceptual Enhancement for Medical Image Analysis | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000864195200007 | - |
dc.identifier.doi | 10.1109/JBHI.2022.3168604 | - |
dc.identifier.bibliographicCitation | IEEE Journal of Biomedical and Health Informatics, v.26, no.10, pp.4826 - 4836 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85128624014 | - |
dc.citation.endPage | 4836 | - |
dc.citation.startPage | 4826 | - |
dc.citation.title | IEEE Journal of Biomedical and Health Informatics | - |
dc.citation.volume | 26 | - |
dc.citation.number | 10 | - |
dc.contributor.affiliatedAuthor | Loh, Woong-Kee | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | Image analysis | - |
dc.subject.keywordAuthor | Image enhancement | - |
dc.subject.keywordAuthor | image processing | - |
dc.subject.keywordAuthor | Kernel | - |
dc.subject.keywordAuthor | Medical diagnostic imaging | - |
dc.subject.keywordAuthor | medical image analysis | - |
dc.subject.keywordAuthor | Medical image enhancement | - |
dc.subject.keywordAuthor | perceptual enhancement | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Visualization | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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