Effective R2 map-based liver segmentation method in an MR image
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Eun, S.-J. | - |
dc.contributor.author | Kwon, J. | - |
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Whangbo, T.-K. | - |
dc.date.available | 2020-02-29T09:44:28Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17459 | - |
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 region 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 8.5%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained. © 2012 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.relation.isPartOf | 2012 International Conference on Information Science and Applications, ICISA 2012 | - |
dc.subject | Computer processing | - |
dc.subject | Liver regions | - |
dc.subject | Liver segmentation | - |
dc.subject | MR images | - |
dc.subject | Pattern analysis | - |
dc.subject | Region growing | - |
dc.subject | Region segmentation | - |
dc.subject | Seed point | - |
dc.subject | Texture analysis | - |
dc.subject | D region | - |
dc.subject | Image texture | - |
dc.subject | Information science | - |
dc.subject | Magnetic resonance | - |
dc.subject | Object recognition | - |
dc.subject | Three dimensional | - |
dc.subject | Image segmentation | - |
dc.title | Effective R2 map-based liver segmentation method in an MR image | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/ICISA.2012.6220957 | - |
dc.identifier.bibliographicCitation | 2012 International Conference on Information Science and Applications, ICISA 2012 | - |
dc.identifier.scopusid | 2-s2.0-84864202767 | - |
dc.citation.title | 2012 International Conference on Information Science and Applications, ICISA 2012 | - |
dc.contributor.affiliatedAuthor | Eun, S.-J. | - |
dc.contributor.affiliatedAuthor | Whangbo, T.-K. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | 3D region growing | - |
dc.subject.keywordAuthor | component | - |
dc.subject.keywordAuthor | MR image | - |
dc.subject.keywordAuthor | Liver segmentation | - |
dc.subject.keywordAuthor | R2 map | - |
dc.subject.keywordAuthor | Texture analysis | - |
dc.subject.keywordPlus | Computer processing | - |
dc.subject.keywordPlus | Liver regions | - |
dc.subject.keywordPlus | Liver segmentation | - |
dc.subject.keywordPlus | MR images | - |
dc.subject.keywordPlus | Pattern analysis | - |
dc.subject.keywordPlus | Region growing | - |
dc.subject.keywordPlus | Region segmentation | - |
dc.subject.keywordPlus | Seed point | - |
dc.subject.keywordPlus | Texture analysis | - |
dc.subject.keywordPlus | D region | - |
dc.subject.keywordPlus | Image texture | - |
dc.subject.keywordPlus | Information science | - |
dc.subject.keywordPlus | Magnetic resonance | - |
dc.subject.keywordPlus | Object recognition | - |
dc.subject.keywordPlus | Three dimensional | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.