Lung cancer pathology discrimination techniques using time series analysis
The goal of this study is to discover, analyze, compare, and interpret diffused reflectance polarimetric signatures from lung cancer cells through time series analysis techniques, by using recently invented efficient polarimetric backscattering detection techniques. Specifically, different time seri...
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| creator | Farrahi, T. Giakos, G. Quang, T. Shrestha, S. Deshpande, A. Narayan, C. Karras, D. A. |
| description | The goal of this study is to discover, analyze, compare, and interpret diffused reflectance polarimetric signatures from lung cancer cells through time series analysis techniques, by using recently invented efficient polarimetric backscattering detection techniques. Specifically, different time series analyses, relying on linear and generalized linear modeling, have been investigated, with special emphasis on the Granger test for the time series. The experimental results indicate that statistically enhanced discrimination between normal and different types of lung cancer cells and stages can be achieved based on the pairwise comparisons of the time series diffused reflectance signal intensities and depolarization properties of the cells. |
| doi_str_mv | 10.1109/IST.2013.6729667 |
| format | Conference Proceeding |
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A.</creatorcontrib><title>Lung cancer pathology discrimination techniques using time series analysis</title><title>2013 IEEE International Conference on Imaging Systems and Techniques (IST)</title><addtitle>IST</addtitle><description>The goal of this study is to discover, analyze, compare, and interpret diffused reflectance polarimetric signatures from lung cancer cells through time series analysis techniques, by using recently invented efficient polarimetric backscattering detection techniques. Specifically, different time series analyses, relying on linear and generalized linear modeling, have been investigated, with special emphasis on the Granger test for the time series. The experimental results indicate that statistically enhanced discrimination between normal and different types of lung cancer cells and stages can be achieved based on the pairwise comparisons of the time series diffused reflectance signal intensities and depolarization properties of the cells.</description><subject>adenocarcinoma</subject><subject>Autoregressive processes</subject><subject>Cancer</subject><subject>Correlation</subject><subject>correlation analysis</subject><subject>Histograms</subject><subject>lung cancer detection</subject><subject>Lungs</subject><subject>medical diagnostic techniques</subject><subject>mixture of cancer cells</subject><subject>Reflectivity</subject><subject>squamous carcinoma</subject><subject>Time series analysis</subject><subject>time series analysis of polarimetric signals</subject><issn>1558-2809</issn><issn>2832-4242</issn><isbn>146735791X</isbn><isbn>9781467357913</isbn><isbn>9781467357906</isbn><isbn>1467357901</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkEtLw0AUhUdRMNbuBTf5A6n33nllllJ8VAIurOCuTJKbdiRNaiZZ9N9bsKsDB77DxxHiHmGBCO5x9bleEKBcGEvOGHshblEZK7V1-H0pEsolZYoUXYkEtc4zysHdiHmMPwCA1hitKBHvxdRt08p3FQ_pwY-7vu23x7QOsRrCPnR-DH2XjlztuvA7cUynGE7AGPacRh7CqfGdb48xxDtx3fg28vycM_H18rxevmXFx-tq-VRkgSAfM2cqYMuNzDWWDFSrXCqWzlhG0oSlIw1NUypunGTJWCNoVIBGsbFQy5l4-N8NzLw5nDT9cNycb5B_1pNPPw</recordid><startdate>20131001</startdate><enddate>20131001</enddate><creator>Farrahi, T.</creator><creator>Giakos, G.</creator><creator>Quang, T.</creator><creator>Shrestha, S.</creator><creator>Deshpande, A.</creator><creator>Narayan, C.</creator><creator>Karras, D. A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20131001</creationdate><title>Lung cancer pathology discrimination techniques using time series analysis</title><author>Farrahi, T. ; Giakos, G. ; Quang, T. ; Shrestha, S. ; Deshpande, A. ; Narayan, C. ; Karras, D. 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A.</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>Farrahi, T.</au><au>Giakos, G.</au><au>Quang, T.</au><au>Shrestha, S.</au><au>Deshpande, A.</au><au>Narayan, C.</au><au>Karras, D. A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Lung cancer pathology discrimination techniques using time series analysis</atitle><btitle>2013 IEEE International Conference on Imaging Systems and Techniques (IST)</btitle><stitle>IST</stitle><date>2013-10-01</date><risdate>2013</risdate><spage>79</spage><epage>84</epage><pages>79-84</pages><issn>1558-2809</issn><eissn>2832-4242</eissn><eisbn>146735791X</eisbn><eisbn>9781467357913</eisbn><eisbn>9781467357906</eisbn><eisbn>1467357901</eisbn><abstract>The goal of this study is to discover, analyze, compare, and interpret diffused reflectance polarimetric signatures from lung cancer cells through time series analysis techniques, by using recently invented efficient polarimetric backscattering detection techniques. Specifically, different time series analyses, relying on linear and generalized linear modeling, have been investigated, with special emphasis on the Granger test for the time series. 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| ispartof | 2013 IEEE International Conference on Imaging Systems and Techniques (IST), 2013, p.79-84 |
| issn | 1558-2809 2832-4242 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | adenocarcinoma Autoregressive processes Cancer Correlation correlation analysis Histograms lung cancer detection Lungs medical diagnostic techniques mixture of cancer cells Reflectivity squamous carcinoma Time series analysis time series analysis of polarimetric signals |
| title | Lung cancer pathology discrimination techniques using time series analysis |
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