Effective SWI-based brain segmentation method in an MR image
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Eun, S.-J. | - |
dc.contributor.author | Kwon, J. | - |
dc.contributor.author | Whangbo, T.-K. | - |
dc.date.available | 2020-02-29T00:46:36Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 2078-0958 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14860 | - |
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 Susceptibility Weighted Imaging (SWI) within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the brain region and by getting up contrast enhancement image of SWI for texture analysis to enable region (white matter) segmentation even when the border line was not clear. As a result, an average area difference of 7.6%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Newswood Limited | - |
dc.relation.isPartOf | Lecture Notes in Engineering and Computer Science | - |
dc.subject | Brain | - |
dc.subject | Computer science | - |
dc.subject | Curve fitting | - |
dc.subject | Magnetic resonance | - |
dc.subject | Magnetic resonance imaging | - |
dc.subject | Object recognition | - |
dc.subject | Textures | - |
dc.subject | Brain segmentation | - |
dc.subject | Computer processing | - |
dc.subject | Contrast Enhancement | - |
dc.subject | MR images | - |
dc.subject | Pattern analysis | - |
dc.subject | Region segmentation | - |
dc.subject | Susceptibility weighted Imaging | - |
dc.subject | Texture analysis | - |
dc.subject | Image segmentation | - |
dc.title | Effective SWI-based brain segmentation method in an MR image | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Engineering and Computer Science, v.2202, pp.99 - 104 | - |
dc.identifier.scopusid | 2-s2.0-84880075309 | - |
dc.citation.endPage | 104 | - |
dc.citation.startPage | 99 | - |
dc.citation.title | Lecture Notes in Engineering and Computer Science | - |
dc.citation.volume | 2202 | - |
dc.contributor.affiliatedAuthor | Eun, S.-J. | - |
dc.contributor.affiliatedAuthor | Whangbo, T.-K. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Brain segmentation | - |
dc.subject.keywordAuthor | Curve fitting | - |
dc.subject.keywordAuthor | MR image | - |
dc.subject.keywordAuthor | Susceptibility Weighted Imaging (SWI) | - |
dc.subject.keywordAuthor | Texture analysis | - |
dc.subject.keywordPlus | Brain | - |
dc.subject.keywordPlus | Computer science | - |
dc.subject.keywordPlus | Curve fitting | - |
dc.subject.keywordPlus | Magnetic resonance | - |
dc.subject.keywordPlus | Magnetic resonance imaging | - |
dc.subject.keywordPlus | Object recognition | - |
dc.subject.keywordPlus | Textures | - |
dc.subject.keywordPlus | Brain segmentation | - |
dc.subject.keywordPlus | Computer processing | - |
dc.subject.keywordPlus | Contrast Enhancement | - |
dc.subject.keywordPlus | MR images | - |
dc.subject.keywordPlus | Pattern analysis | - |
dc.subject.keywordPlus | Region segmentation | - |
dc.subject.keywordPlus | Susceptibility weighted Imaging | - |
dc.subject.keywordPlus | Texture analysis | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.description.journalRegisteredClass | scopus | - |
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