Effective object segmentation based on physical theory in an MR image
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...
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| Veröffentlicht in: | Multimedia tools and applications 2015-08, Vol.74 (16), p.6273-6286 |
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| container_title | Multimedia tools and applications |
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| creator | Eun, Sung-Jong Kim, Hyeonjin Park, Jung-Wook Whangbo, Taeg-Keun |
| description | 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. |
| doi_str_mv | 10.1007/s11042-014-2089-9 |
| format | Article |
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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. 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Kim, Hyeonjin ; Park, Jung-Wook ; Whangbo, Taeg-Keun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c419t-3be834caf22f00e4caa69a075509302ff2d1ea417f333c379993ac85477d189e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Borders</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Data Structures and Information Theory</topic><topic>Image processing systems</topic><topic>Information technology</topic><topic>Liver</topic><topic>Magnetic resonance imaging</topic><topic>Methods</topic><topic>Multimedia</topic><topic>Multimedia Information Systems</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Object recognition</topic><topic>Pattern analysis</topic><topic>Pattern recognition</topic><topic>Segmentation</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Statistical analysis</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eun, Sung-Jong</creatorcontrib><creatorcontrib>Kim, Hyeonjin</creatorcontrib><creatorcontrib>Park, Jung-Wook</creatorcontrib><creatorcontrib>Whangbo, Taeg-Keun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eun, Sung-Jong</au><au>Kim, Hyeonjin</au><au>Park, Jung-Wook</au><au>Whangbo, Taeg-Keun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effective object segmentation based on physical theory in an MR image</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2015-08-01</date><risdate>2015</risdate><volume>74</volume><issue>16</issue><spage>6273</spage><epage>6286</epage><pages>6273-6286</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-014-2089-9</doi><tpages>14</tpages></addata></record> |
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| subjects | Accuracy Algorithms Analysis Borders Computer Communication Networks Computer Science Data Structures and Information Theory Image processing systems Information technology Liver Magnetic resonance imaging Methods Multimedia Multimedia Information Systems NMR Nuclear magnetic resonance Object recognition Pattern analysis Pattern recognition Segmentation Special Purpose and Application-Based Systems Statistical analysis Studies |
| title | Effective object segmentation based on physical theory in an MR image |
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