fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment
There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated...
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| Veröffentlicht in: | Medical & biological engineering & computing 2010-06, Vol.48 (6), p.579-588 |
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| creator | Hsieh, Chi-Wen Liu, Tzu-Chiang Jong, Tai-Lang Tiu, Chui-Mei |
| description | There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. The study shows that the proposed model based on fuzzy rule has an accuracy rate above 89% in Type-I and II, and above 87% in Type-III within a tolerance of 1.5 years. |
| doi_str_mv | 10.1007/s11517-010-0609-y |
| format | Article |
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Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. The study shows that the proposed model based on fuzzy rule has an accuracy rate above 89% in Type-I and II, and above 87% in Type-III within a tolerance of 1.5 years.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-010-0609-y</identifier><identifier>PMID: 20405228</identifier><language>eng</language><publisher>Berlin/Heidelberg: Berlin/Heidelberg : Springer-Verlag</publisher><subject>Age ; Age Determination by Skeleton - methods ; Algorithms ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Bone age assessment ; Bones ; Carpal Bones - diagnostic imaging ; Carpal Bones - growth & development ; Carpal growth modeling ; Child ; Child, Preschool ; Computer Applications ; Feature capture ; Female ; Fuzzy Logic ; Growth models ; Human Physiology ; Humans ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Infant ; Male ; Methods ; Morphology ; Neural networks ; Original Article ; Pediatrics ; Physical growth ; Principal Component Analysis ; Principal components analysis ; Principle component analysis ; Radiology ; Sex Characteristics ; Statistical analysis ; Studies</subject><ispartof>Medical & biological engineering & computing, 2010-06, Vol.48 (6), p.579-588</ispartof><rights>International Federation for Medical and Biological Engineering 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-c3135c37b991f7c41f3ef2bc709419be3cb98520c7e5066e2f8ad50fc22df5b13</citedby><cites>FETCH-LOGICAL-c426t-c3135c37b991f7c41f3ef2bc709419be3cb98520c7e5066e2f8ad50fc22df5b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-010-0609-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-010-0609-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20405228$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hsieh, Chi-Wen</creatorcontrib><creatorcontrib>Liu, Tzu-Chiang</creatorcontrib><creatorcontrib>Jong, Tai-Lang</creatorcontrib><creatorcontrib>Tiu, Chui-Mei</creatorcontrib><title>fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. The study shows that the proposed model based on fuzzy rule has an accuracy rate above 89% in Type-I and II, and above 87% in Type-III within a tolerance of 1.5 years.</description><subject>Age</subject><subject>Age Determination by Skeleton - methods</subject><subject>Algorithms</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Bone age assessment</subject><subject>Bones</subject><subject>Carpal Bones - diagnostic imaging</subject><subject>Carpal Bones - growth & development</subject><subject>Carpal growth modeling</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Computer Applications</subject><subject>Feature capture</subject><subject>Female</subject><subject>Fuzzy Logic</subject><subject>Growth models</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Infant</subject><subject>Male</subject><subject>Methods</subject><subject>Morphology</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Pediatrics</subject><subject>Physical growth</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Principle component analysis</subject><subject>Radiology</subject><subject>Sex Characteristics</subject><subject>Statistical analysis</subject><subject>Studies</subject><issn>0140-0118</issn><issn>1741-0444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkU2L1TAUhoMozp3RH-BGg5tZVc_JR9MsZfALBlzorEOaJtcObVOTlqHz682lo4ILXeVAnvc9cB5CXiC8QQD1NiNKVBUgVFCDrrZH5IBKYAVCiMfkACjKD2JzRs5zvgVgKJl4Ss4YCJCMNQfiwnp_v1Wtzb6jxxTvlu90jJ0f6F1fxjn1k-vnwVMXxzlOflqoneyw5T7T7Afvlj5ONMREnU2zHWhboMoePbU5-5zHknhGngQ7ZP_84b0gNx_ef7v6VF1_-fj56t115QSrl8px5NJx1WqNQTmBgfvAWqdAC9St567VjWTglJdQ156FxnYSgmOsC7JFfkEu9945xR-rz4sZ--z8MNjJxzUbJUSNGpv6_yTnTCvJZSFf_0XexjWVC2TDlSyFWukC4Q65FHNOPphyt9GmzSCYkymzmzLFlDmZMlvJvHwoXtvRd78Tv9QUgO1APkk4-vRn879aX-2hYKOxx9Rnc_OVAXLARgrQnP8EfuOonw</recordid><startdate>20100601</startdate><enddate>20100601</enddate><creator>Hsieh, Chi-Wen</creator><creator>Liu, Tzu-Chiang</creator><creator>Jong, Tai-Lang</creator><creator>Tiu, Chui-Mei</creator><general>Berlin/Heidelberg : 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Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2010-06-01</date><risdate>2010</risdate><volume>48</volume><issue>6</issue><spage>579</spage><epage>588</epage><pages>579-588</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. 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| subjects | Age Age Determination by Skeleton - methods Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Bone age assessment Bones Carpal Bones - diagnostic imaging Carpal Bones - growth & development Carpal growth modeling Child Child, Preschool Computer Applications Feature capture Female Fuzzy Logic Growth models Human Physiology Humans Image Interpretation, Computer-Assisted - methods Imaging Infant Male Methods Morphology Neural networks Original Article Pediatrics Physical growth Principal Component Analysis Principal components analysis Principle component analysis Radiology Sex Characteristics Statistical analysis Studies |
| title | fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment |
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