Feature extraction of X-ray chest image based on KPCA

Feature extraction of X-ray chest image based on KPCA

In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Wencheng Cui, Shuang Chen, Tianshu Yu, Lijie Ren
Format: Tagungsbericht
Sprache:eng
Schlagworte:
feature extraction
kernel principal component analysis
reduce dimensions
X-ray image
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1266
container_issue
container_start_page 1263
container_title
container_volume
creator Wencheng Cui
Shuang Chen
Tianshu Yu
Lijie Ren
description In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space where dimension reduction is implemented and features are extracted, and then nonlinear information is converted to linear information in the feature space. This method reduces feature dimension considerably while it maintains adequate original X-ray chest image information. Experimental results show that this method can enhance retrieval accuracy and has better performance than principal component analysis.
doi_str_mv 10.1109/ICCSNT.2012.6526153
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6526153</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6526153</ieee_id><sourcerecordid>6526153</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-8af7e95cdab7232652598a3d5fe649b8ae0164d52dbb435eaa387d6ee98efd4a3</originalsourceid><addsrcrecordid>eNpFj9tKAzEYhCMiqLVP0Ju8wK45Hy5LsFosWmgF78q_mz-6olaSCPbtXbDg1TAMM3xDyIyzlnPmr5chbB62rWBctEYLw7U8IZdcGSuFN8qf_hvJzsm0lDfG2Fg11skLohcI9TsjxZ-aoa_D_pPuE31uMhxo_4ql0uEDXpB2UDDSMb1fh_kVOUvwXnB61Al5Wtxsw12zerxdhvmqGbjVtXGQLHrdR-iskGLE096BjDrhSNY5QMaNilrErlNSI4B0NhpE7zBFBXJCZn-7AyLuvvKIkg-74035C4HsRdI</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Feature extraction of X-ray chest image based on KPCA</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Wencheng Cui ; Shuang Chen ; Tianshu Yu ; Lijie Ren</creator><creatorcontrib>Wencheng Cui ; Shuang Chen ; Tianshu Yu ; Lijie Ren</creatorcontrib><description>In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space where dimension reduction is implemented and features are extracted, and then nonlinear information is converted to linear information in the feature space. This method reduces feature dimension considerably while it maintains adequate original X-ray chest image information. Experimental results show that this method can enhance retrieval accuracy and has better performance than principal component analysis.</description><identifier>ISBN: 1467329630</identifier><identifier>ISBN: 9781467329637</identifier><identifier>EISBN: 1467329649</identifier><identifier>EISBN: 9781467329644</identifier><identifier>EISBN: 9781467329620</identifier><identifier>EISBN: 1467329622</identifier><identifier>DOI: 10.1109/ICCSNT.2012.6526153</identifier><language>eng</language><publisher>IEEE</publisher><subject>feature extraction ; kernel principal component analysis ; reduce dimensions ; X-ray image</subject><ispartof>Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, 2012, p.1263-1266</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/6526153$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6526153$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wencheng Cui</creatorcontrib><creatorcontrib>Shuang Chen</creatorcontrib><creatorcontrib>Tianshu Yu</creatorcontrib><creatorcontrib>Lijie Ren</creatorcontrib><title>Feature extraction of X-ray chest image based on KPCA</title><title>Proceedings of 2012 2nd International Conference on Computer Science and Network Technology</title><addtitle>ICCSNT</addtitle><description>In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space where dimension reduction is implemented and features are extracted, and then nonlinear information is converted to linear information in the feature space. This method reduces feature dimension considerably while it maintains adequate original X-ray chest image information. Experimental results show that this method can enhance retrieval accuracy and has better performance than principal component analysis.</description><subject>feature extraction</subject><subject>kernel principal component analysis</subject><subject>reduce dimensions</subject><subject>X-ray image</subject><isbn>1467329630</isbn><isbn>9781467329637</isbn><isbn>1467329649</isbn><isbn>9781467329644</isbn><isbn>9781467329620</isbn><isbn>1467329622</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj9tKAzEYhCMiqLVP0Ju8wK45Hy5LsFosWmgF78q_mz-6olaSCPbtXbDg1TAMM3xDyIyzlnPmr5chbB62rWBctEYLw7U8IZdcGSuFN8qf_hvJzsm0lDfG2Fg11skLohcI9TsjxZ-aoa_D_pPuE31uMhxo_4ql0uEDXpB2UDDSMb1fh_kVOUvwXnB61Al5Wtxsw12zerxdhvmqGbjVtXGQLHrdR-iskGLE096BjDrhSNY5QMaNilrErlNSI4B0NhpE7zBFBXJCZn-7AyLuvvKIkg-74035C4HsRdI</recordid><startdate>201212</startdate><enddate>201212</enddate><creator>Wencheng Cui</creator><creator>Shuang Chen</creator><creator>Tianshu Yu</creator><creator>Lijie Ren</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201212</creationdate><title>Feature extraction of X-ray chest image based on KPCA</title><author>Wencheng Cui ; Shuang Chen ; Tianshu Yu ; Lijie Ren</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8af7e95cdab7232652598a3d5fe649b8ae0164d52dbb435eaa387d6ee98efd4a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>feature extraction</topic><topic>kernel principal component analysis</topic><topic>reduce dimensions</topic><topic>X-ray image</topic><toplevel>online_resources</toplevel><creatorcontrib>Wencheng Cui</creatorcontrib><creatorcontrib>Shuang Chen</creatorcontrib><creatorcontrib>Tianshu Yu</creatorcontrib><creatorcontrib>Lijie Ren</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>Wencheng Cui</au><au>Shuang Chen</au><au>Tianshu Yu</au><au>Lijie Ren</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Feature extraction of X-ray chest image based on KPCA</atitle><btitle>Proceedings of 2012 2nd International Conference on Computer Science and Network Technology</btitle><stitle>ICCSNT</stitle><date>2012-12</date><risdate>2012</risdate><spage>1263</spage><epage>1266</epage><pages>1263-1266</pages><isbn>1467329630</isbn><isbn>9781467329637</isbn><eisbn>1467329649</eisbn><eisbn>9781467329644</eisbn><eisbn>9781467329620</eisbn><eisbn>1467329622</eisbn><abstract>In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space where dimension reduction is implemented and features are extracted, and then nonlinear information is converted to linear information in the feature space. This method reduces feature dimension considerably while it maintains adequate original X-ray chest image information. Experimental results show that this method can enhance retrieval accuracy and has better performance than principal component analysis.</abstract><pub>IEEE</pub><doi>10.1109/ICCSNT.2012.6526153</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 1467329630
ispartof Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, 2012, p.1263-1266
issn
language eng
recordid cdi_ieee_primary_6526153
source IEEE Electronic Library (IEL) Conference Proceedings
subjects feature extraction
kernel principal component analysis
reduce dimensions
X-ray image
title Feature extraction of X-ray chest image based on KPCA
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T04%3A22%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Feature%20extraction%20of%20X-ray%20chest%20image%20based%20on%20KPCA&rft.btitle=Proceedings%20of%202012%202nd%20International%20Conference%20on%20Computer%20Science%20and%20Network%20Technology&rft.au=Wencheng%20Cui&rft.date=2012-12&rft.spage=1263&rft.epage=1266&rft.pages=1263-1266&rft.isbn=1467329630&rft.isbn_list=9781467329637&rft_id=info:doi/10.1109/ICCSNT.2012.6526153&rft_dat=%3Cieee_6IE%3E6526153%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467329649&rft.eisbn_list=9781467329644&rft.eisbn_list=9781467329620&rft.eisbn_list=1467329622&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6526153&rfr_iscdi=true