Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model
Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance...
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| creator | Alomari, R.S. Corso, J.J. Chaudhary, V. Dhillon, G. |
| description | Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment. |
| doi_str_mv | 10.1109/ISBI.2009.5193105 |
| format | Conference Proceeding |
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Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. 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Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment.</description><subject>Back</subject><subject>Computer Aided Diagnosis</subject><subject>Computer science</subject><subject>Context modeling</subject><subject>Degenerative diseases</subject><subject>Desiccation Diagnosis</subject><subject>Lumbar discs</subject><subject>Magnetic field measurement</subject><subject>Magnetic resonance imaging</subject><subject>MRI</subject><subject>Neural networks</subject><subject>Pain</subject><subject>Spine</subject><subject>Testing</subject><issn>1945-7928</issn><issn>1945-8452</issn><isbn>1424439310</isbn><isbn>9781424439317</isbn><isbn>1424439329</isbn><isbn>9781424439324</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMtKxDAYheMNHMd5AHGTF2jNte2_1PFWGBFGXQ9JmugvaTs0FfHtLVjwbD44H5zFIeSCs5xzBlf1y02dC8Yg1xwkZ_qAnHEllJIgBRySBQels0ppcfQvODueRQmiOiWrlD7ZlHKyTC3I9tYndM6M2He0QfPe9QkTxY7Gr9aaYeqSSzQMfUtdxA6difRpW9NvHD-oofuht8ZixDSio23f-HhOToKJya9mLsnb_d3r-jHbPD_U6-tNhoJXYxaYCgBGOF0pU0qowDcgAtMWArelY4UOznhf6sIGEaSSVkrVVFyCgLJQckku_3bRe7_bD9ia4Wc3fyN_AbV5U4Q</recordid><startdate>20090101</startdate><enddate>20090101</enddate><creator>Alomari, R.S.</creator><creator>Corso, J.J.</creator><creator>Chaudhary, V.</creator><creator>Dhillon, G.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20090101</creationdate><title>Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model</title><author>Alomari, R.S. ; Corso, J.J. ; Chaudhary, V. ; Dhillon, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i218t-f04f99a2c584a73989ed92f05b9f1b7c065fcaee756bf2f343b334d8139297643</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Back</topic><topic>Computer Aided Diagnosis</topic><topic>Computer science</topic><topic>Context modeling</topic><topic>Degenerative diseases</topic><topic>Desiccation Diagnosis</topic><topic>Lumbar discs</topic><topic>Magnetic field measurement</topic><topic>Magnetic resonance imaging</topic><topic>MRI</topic><topic>Neural networks</topic><topic>Pain</topic><topic>Spine</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Alomari, R.S.</creatorcontrib><creatorcontrib>Corso, J.J.</creatorcontrib><creatorcontrib>Chaudhary, V.</creatorcontrib><creatorcontrib>Dhillon, G.</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>Alomari, R.S.</au><au>Corso, J.J.</au><au>Chaudhary, V.</au><au>Dhillon, G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model</atitle><btitle>2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</btitle><stitle>ISBI</stitle><date>2009-01-01</date><risdate>2009</risdate><spage>546</spage><epage>549</epage><pages>546-549</pages><issn>1945-7928</issn><eissn>1945-8452</eissn><isbn>1424439310</isbn><isbn>9781424439317</isbn><eisbn>1424439329</eisbn><eisbn>9781424439324</eisbn><abstract>Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment.</abstract><pub>IEEE</pub><doi>10.1109/ISBI.2009.5193105</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, p.546-549 |
| issn | 1945-7928 1945-8452 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Back Computer Aided Diagnosis Computer science Context modeling Degenerative diseases Desiccation Diagnosis Lumbar discs Magnetic field measurement Magnetic resonance imaging MRI Neural networks Pain Spine Testing |
| title | Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model |
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