Assessing Torso Deformity in Scoliosis Using Self-Organizing Neural Networks (SNN)
This paper presents a novel technique for parameterizing malformation of the torso in scoliosis. Scoliosis is complex 3 dimensional deformity of the spine in which the trunk distorts because of the internal spinal deformity. Thus monitoring the distortion in the external torso would be beneficial in...
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| creator | Igwe, P. Emrani, M. Adeeb, S. Hill, D. |
| description | This paper presents a novel technique for parameterizing malformation of the torso in scoliosis. Scoliosis is complex 3 dimensional deformity of the spine in which the trunk distorts because of the internal spinal deformity. Thus monitoring the distortion in the external torso would be beneficial in tracking important changes and possibly predicting the underlying changes in the internal spine. The technique proposed in this paper, facilitates torso surface assessment and it consists of three stages of digitizing, parameterizing and mapping. Self-organizing neural networks (SNN) is used to parameterize the torso malformation. The orientation and position of the neuron in the SNN provides detailed insight regarding significant changes in the torso model. Preliminary results are presented to further illustrate the capability of the technique. |
| doi_str_mv | 10.1109/ICMLA.2008.68 |
| format | Conference Proceeding |
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Scoliosis is complex 3 dimensional deformity of the spine in which the trunk distorts because of the internal spinal deformity. Thus monitoring the distortion in the external torso would be beneficial in tracking important changes and possibly predicting the underlying changes in the internal spine. The technique proposed in this paper, facilitates torso surface assessment and it consists of three stages of digitizing, parameterizing and mapping. Self-organizing neural networks (SNN) is used to parameterize the torso malformation. The orientation and position of the neuron in the SNN provides detailed insight regarding significant changes in the torso model. Preliminary results are presented to further illustrate the capability of the technique.</description><identifier>ISBN: 0769534953</identifier><identifier>ISBN: 9780769534954</identifier><identifier>DOI: 10.1109/ICMLA.2008.68</identifier><identifier>LCCN: 2008908513</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Geometry ; Interpolation ; Neural networks ; Optical scattering ; Organizing ; Parameterization ; Scoliosis ; Self-organizing neural networks ; Shape ; Shape transformation ; Surface reconstruction ; Surface topography ; Torso</subject><ispartof>2008 Seventh International Conference on Machine Learning and Applications, 2008, p.497-502</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4725019$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4725019$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Igwe, P.</creatorcontrib><creatorcontrib>Emrani, M.</creatorcontrib><creatorcontrib>Adeeb, S.</creatorcontrib><creatorcontrib>Hill, D.</creatorcontrib><title>Assessing Torso Deformity in Scoliosis Using Self-Organizing Neural Networks (SNN)</title><title>2008 Seventh International Conference on Machine Learning and Applications</title><addtitle>ICMLA</addtitle><description>This paper presents a novel technique for parameterizing malformation of the torso in scoliosis. Scoliosis is complex 3 dimensional deformity of the spine in which the trunk distorts because of the internal spinal deformity. Thus monitoring the distortion in the external torso would be beneficial in tracking important changes and possibly predicting the underlying changes in the internal spine. The technique proposed in this paper, facilitates torso surface assessment and it consists of three stages of digitizing, parameterizing and mapping. Self-organizing neural networks (SNN) is used to parameterize the torso malformation. The orientation and position of the neuron in the SNN provides detailed insight regarding significant changes in the torso model. Preliminary results are presented to further illustrate the capability of the technique.</description><subject>Artificial neural networks</subject><subject>Geometry</subject><subject>Interpolation</subject><subject>Neural networks</subject><subject>Optical scattering</subject><subject>Organizing</subject><subject>Parameterization</subject><subject>Scoliosis</subject><subject>Self-organizing neural networks</subject><subject>Shape</subject><subject>Shape transformation</subject><subject>Surface reconstruction</subject><subject>Surface topography</subject><subject>Torso</subject><isbn>0769534953</isbn><isbn>9780769534954</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT81LwzAcDchAN3f05KVHPbT-kjRNcizV6aB2YOt5tPkY0a6VZCLzr7dTD-893uPx4CF0hSHBGOTdungu84QAiCQTZ2gOPJOMphNmaH6KJQiG6TlahvAGAFhmHDNxgV7yEEwIbthFzejDGN0bO_q9OxwjN0S1Gns3Bhei199KbXobb_yuHdz3yVfm07f9JIev0b-H6KauqttLNLNtH8zyXxeoWT00xVNcbh7XRV7GDnN2iEUnSQoEBGEMa5ZNpFOqmbIcW1Ciw5RoxWWLBVWi1WCF6hgBzXAqgdAFuv6bdcaY7Yd3-9YftyknbHpHfwDICE34</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Igwe, P.</creator><creator>Emrani, M.</creator><creator>Adeeb, S.</creator><creator>Hill, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Assessing Torso Deformity in Scoliosis Using Self-Organizing Neural Networks (SNN)</title><author>Igwe, P. ; Emrani, M. ; Adeeb, S. ; Hill, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8b92402082551d5651dd43d5cf71f0c8b132dc79a183c8ad0f8cb520d5149023</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial neural networks</topic><topic>Geometry</topic><topic>Interpolation</topic><topic>Neural networks</topic><topic>Optical scattering</topic><topic>Organizing</topic><topic>Parameterization</topic><topic>Scoliosis</topic><topic>Self-organizing neural networks</topic><topic>Shape</topic><topic>Shape transformation</topic><topic>Surface reconstruction</topic><topic>Surface topography</topic><topic>Torso</topic><toplevel>online_resources</toplevel><creatorcontrib>Igwe, P.</creatorcontrib><creatorcontrib>Emrani, M.</creatorcontrib><creatorcontrib>Adeeb, S.</creatorcontrib><creatorcontrib>Hill, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Igwe, P.</au><au>Emrani, M.</au><au>Adeeb, S.</au><au>Hill, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Assessing Torso Deformity in Scoliosis Using Self-Organizing Neural Networks (SNN)</atitle><btitle>2008 Seventh International Conference on Machine Learning and Applications</btitle><stitle>ICMLA</stitle><date>2008-12</date><risdate>2008</risdate><spage>497</spage><epage>502</epage><pages>497-502</pages><isbn>0769534953</isbn><isbn>9780769534954</isbn><abstract>This paper presents a novel technique for parameterizing malformation of the torso in scoliosis. Scoliosis is complex 3 dimensional deformity of the spine in which the trunk distorts because of the internal spinal deformity. Thus monitoring the distortion in the external torso would be beneficial in tracking important changes and possibly predicting the underlying changes in the internal spine. The technique proposed in this paper, facilitates torso surface assessment and it consists of three stages of digitizing, parameterizing and mapping. Self-organizing neural networks (SNN) is used to parameterize the torso malformation. The orientation and position of the neuron in the SNN provides detailed insight regarding significant changes in the torso model. Preliminary results are presented to further illustrate the capability of the technique.</abstract><pub>IEEE</pub><doi>10.1109/ICMLA.2008.68</doi><tpages>6</tpages></addata></record> |
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| subjects | Artificial neural networks Geometry Interpolation Neural networks Optical scattering Organizing Parameterization Scoliosis Self-organizing neural networks Shape Shape transformation Surface reconstruction Surface topography Torso |
| title | Assessing Torso Deformity in Scoliosis Using Self-Organizing Neural Networks (SNN) |
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