Effective object segmentation based on physical theory in an MR image
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Eun, Sung-Jong | - |
dc.contributor.author | Kim, Hyeonjin | - |
dc.contributor.author | Park, Jung-Wook | - |
dc.contributor.author | Whangbo, Taeg-Keun | - |
dc.date.available | 2020-02-28T08:44:33Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2015-08 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/10269 | - |
dc.description.abstract | Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective object segmentation method based on R2 information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2 map as seed points for region growing to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5 %, which was higher than the accuracy of conventional region segmentation algorithm, was obtained. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.subject | BRAIN | - |
dc.subject | CT | - |
dc.subject | VENOGRAPHY | - |
dc.subject | EXTRACTION | - |
dc.subject | MAXIMUM | - |
dc.title | Effective object segmentation based on physical theory in an MR image | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000359864700010 | - |
dc.identifier.doi | 10.1007/s11042-014-2089-9 | - |
dc.identifier.bibliographicCitation | MULTIMEDIA TOOLS AND APPLICATIONS, v.74, no.16, pp.6273 - 6286 | - |
dc.identifier.scopusid | 2-s2.0-84939256780 | - |
dc.citation.endPage | 6286 | - |
dc.citation.startPage | 6273 | - |
dc.citation.title | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.citation.volume | 74 | - |
dc.citation.number | 16 | - |
dc.contributor.affiliatedAuthor | Eun, Sung-Jong | - |
dc.contributor.affiliatedAuthor | Whangbo, Taeg-Keun | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | MR image | - |
dc.subject.keywordAuthor | Object segmentation | - |
dc.subject.keywordAuthor | R2 map | - |
dc.subject.keywordAuthor | SWI | - |
dc.subject.keywordAuthor | 3D region growing | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | CT | - |
dc.subject.keywordPlus | VENOGRAPHY | - |
dc.subject.keywordPlus | EXTRACTION | - |
dc.subject.keywordPlus | MAXIMUM | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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