Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting
This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOI...
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| container_volume | 2012 |
| creator | Ziming Zeng Shepherd, T. Zwiggelaar, R. |
| description | This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches. |
| doi_str_mv | 10.1109/EMBC.2012.6346432 |
| format | Conference Proceeding |
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The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.</description><identifier>ISSN: 1094-687X</identifier><identifier>ISSN: 1557-170X</identifier><identifier>ISBN: 1424441196</identifier><identifier>ISBN: 9781424441198</identifier><identifier>EISSN: 1558-4615</identifier><identifier>EISBN: 9781457717871</identifier><identifier>EISBN: 1457717875</identifier><identifier>DOI: 10.1109/EMBC.2012.6346432</identifier><identifier>PMID: 23366393</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Artificial Intelligence ; Cancer ; Head and Neck Neoplasms - diagnostic imaging ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image resolution ; Image segmentation ; Imaging phantoms ; Pattern Recognition, Automated - methods ; Phantoms ; Positron emission tomography ; Positron-Emission Tomography - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Tumors</subject><ispartof>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, Vol.2012, p.2339-2342</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/6346432$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6346432$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23366393$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ziming Zeng</creatorcontrib><creatorcontrib>Shepherd, T.</creatorcontrib><creatorcontrib>Zwiggelaar, R.</creatorcontrib><title>Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting</title><title>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>EMBC</addtitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><description>This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.</description><subject>Artificial Intelligence</subject><subject>Cancer</subject><subject>Head and Neck Neoplasms - diagnostic imaging</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Imaging phantoms</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Phantoms</subject><subject>Positron emission tomography</subject><subject>Positron-Emission Tomography - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Tumors</subject><issn>1094-687X</issn><issn>1557-170X</issn><issn>1558-4615</issn><isbn>1424441196</isbn><isbn>9781424441198</isbn><isbn>9781457717871</isbn><isbn>1457717875</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNo9kMtOwzAQRc1LtJR-AEJC_oEUO37FS6jKQyqCRSuxqxxnUowSp4qdSv170haYzczVPRrpXoRuKJlQSvT97O1xOkkJTSeScclZeoLGWmWUC6WoyhQ9RUMqRJZwScUZuqI85ZxTquV5bxDNE5mpzwEah_BN-sloxgi_RIOUMSmZZkMUlz50G2i3LkCBY1c3XYsDrGvw0UTXeOw8_pgtcG72QK-rxpoKG1_gddXk_el8BB9c3OHSxej8Ghsb3RZw6NrSWDiwptp8GVybA3CNLkpTBRj_7hFaPs0W05dk_v78On2YJ44RGpN9FGOVAmNkCdqU1FrNZB-kACVSlgpbQGkK2DeSQ9-MzTkRinGqrSgyNkJ3x7-bLq-hWG1aV5t2t_qL3wO3R8ABwL_9Wzb7ARmdbaM</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Ziming Zeng</creator><creator>Shepherd, T.</creator><creator>Zwiggelaar, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>20120101</creationdate><title>Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting</title><author>Ziming Zeng ; Shepherd, T. ; Zwiggelaar, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i301t-4411ac77eaa6fe9af1cc936000de752325cdefade8145be978cb40573419c5d83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Artificial Intelligence</topic><topic>Cancer</topic><topic>Head and Neck Neoplasms - diagnostic imaging</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Imaging phantoms</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Phantoms</topic><topic>Positron emission tomography</topic><topic>Positron-Emission Tomography - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Tumors</topic><toplevel>online_resources</toplevel><creatorcontrib>Ziming Zeng</creatorcontrib><creatorcontrib>Shepherd, T.</creatorcontrib><creatorcontrib>Zwiggelaar, R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ziming Zeng</au><au>Shepherd, T.</au><au>Zwiggelaar, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting</atitle><btitle>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>EMBC</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2012-01-01</date><risdate>2012</risdate><volume>2012</volume><spage>2339</spage><epage>2342</epage><pages>2339-2342</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>1424441196</isbn><isbn>9781424441198</isbn><eisbn>9781457717871</eisbn><eisbn>1457717875</eisbn><abstract>This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>23366393</pmid><doi>10.1109/EMBC.2012.6346432</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1094-687X |
| ispartof | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, Vol.2012, p.2339-2342 |
| issn | 1094-687X 1557-170X 1558-4615 |
| language | eng |
| recordid | cdi_ieee_primary_6346432 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial Intelligence Cancer Head and Neck Neoplasms - diagnostic imaging Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image resolution Image segmentation Imaging phantoms Pattern Recognition, Automated - methods Phantoms Positron emission tomography Positron-Emission Tomography - methods Reproducibility of Results Sensitivity and Specificity Tumors |
| title | Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting |
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