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MR Weighted Image Discrimination by Artificial Intelligence

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dc.contributor.author이훈재-
dc.date.accessioned2020-11-05T07:21:39Z-
dc.date.available2020-11-05T07:21:39Z-
dc.date.issued2019-12-
dc.identifier.issn2635-4608-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179830-
dc.description.abstractIn this study, we proposed a method of learning neural networks by optimizing neural network input parameters to discern MRI-weighted images. To this end, we segmented the weighting domain of MRI. In feature extraction, the original image and segmented image were extracted by DWT, respectively. A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological technologist work together to provide a solution that is integrated with the radiologist's workflow.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherScholargen International Cooperation-
dc.relation.isPartOfScholarGen Journal of Medical Imaging-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMR Weighted Image Discrimination by Artificial Intelligence-
dc.typeArticle-
dc.contributor.collegeResearch Institutes (연구소)-
dc.contributor.departmentInstitute for Medical Convergence (연의-생공연 메디컬융합연구소)-
dc.contributor.googleauthorGiljae Lee-
dc.contributor.googleauthorGyehwan Jin-
dc.contributor.googleauthorHwunjae Lee-
dc.contributor.googleauthorJaeeun Jung-
dc.identifier.doi10.31916/sjmi2019-01-02-
dc.contributor.localIdA03346-
dc.relation.journalcodeJ03901-
dc.subject.keywordImage processing-
dc.subject.keywordDiscrete Wavelet Transform-
dc.subject.keywordMR pulse sequence-
dc.subject.keywordT2 Weighted Image-
dc.subject.keywordMR Molecular Imaging-
dc.subject.keywordMagnetic nanoparticles-
dc.contributor.alternativeNameLee, Hwun Jae-
dc.contributor.affiliatedAuthor이훈재-
dc.citation.volume3-
dc.citation.number11-
dc.citation.startPage33-
dc.identifier.bibliographicCitationScholarGen Journal of Medical Imaging, Vol.3(11) : 33, 2019-12-
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers

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