{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1362544420182475136.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.4236/ojmi.2017.73010"}},{"identifier":{"@type":"URI","@value":"https://www.scirp.org/journal/paperinformation?paperid=79109"}},{"identifier":{"@type":"URI","@value":"https://www.scirp.org/xml/79109.xml"}}],"dc:title":[{"@value":"Performance Evaluation of Super-Resolution Methods Using Deep-Learning and Sparse-Coding for Improving the Image Quality of Magnified Images in Chest Radiographs"}],"description":[{"notation":[{"@value":"Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p < 0.001 or p < 0.05). The image quality differences between the super-resolution methods were not statistically significant. However, the SRCNN computation time was significantly faster than that of ScSR (p < 0.001). Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest; thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed."}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1382544420182475140","@type":"Researcher","foaf:name":[{"@value":"Kensuke Umehara"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475142","@type":"Researcher","foaf:name":[{"@value":"Junko Ota"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475137","@type":"Researcher","foaf:name":[{"@value":"Naoki Ishimaru"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475136","@type":"Researcher","foaf:name":[{"@value":"Shunsuke Ohno"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475139","@type":"Researcher","foaf:name":[{"@value":"Kentaro Okamoto"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475141","@type":"Researcher","foaf:name":[{"@value":"Takanori Suzuki"}]},{"@id":"https://cir.nii.ac.jp/crid/1382544420182475138","@type":"Researcher","foaf:name":[{"@value":"Takayuki Ishida"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"21642788"},{"@type":"EISSN","@value":"21642796"}],"prism:publicationName":[{"@value":"Open Journal of Medical Imaging"}],"dc:publisher":[{"@value":"Scientific Research Publishing, Inc."}],"prism:publicationDate":"2017","prism:volume":"07","prism:number":"03","prism:startingPage":"100","prism:endingPage":"111"},"reviewed":"false","dcterms:accessRights":"http://purl.org/coar/access_right/c_abf2","dc:rights":["http://creativecommons.org/licenses/by/4.0/"],"url":[{"@id":"https://www.scirp.org/journal/paperinformation?paperid=79109"},{"@id":"https://www.scirp.org/xml/79109.xml"}],"createdAt":"2017-09-14","modifiedAt":"2026-03-16","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360285706284493440","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT"}]},{"@id":"https://cir.nii.ac.jp/crid/1390566775135128064","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"1. Deep Learning Super-resolution in Medical Imaging: What Is It and How to Use It"},{"@language":"ja","@value":"1. 深層学習を用いた超解像技術と医用画像への応用"},{"@value":"教育講座 超画像処理技術(1)深層学習を用いた超解像技術と医用画像への応用"},{"@language":"ja-Kana","@value":"キョウイク コウザ チョウガゾウ ショリ ギジュツ(1)シンソウ ガクシュウ オ モチイタ チョウカイゾウ ギジュツ ト イヨウ ガゾウ エ ノ オウヨウ"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.4236/ojmi.2017.73010"},{"@type":"OPENAIRE","@value":"doi_dedup___::0432c22713159fb762a20883d85d6d5d"},{"@type":"CROSSREF","@value":"10.1007/s10278-017-0033-z_references_DOI_VEepWO4sMEvMz7CQiFtNJSeWQSJ"},{"@type":"CROSSREF","@value":"10.6009/jjrt.2020_jsrt_76.5.524_references_DOI_VEepWO4sMEvMz7CQiFtNJSeWQSJ"}]}