Left ventricular wall segmentation in ultrasound cross-sectional images
When ultrasound images are examined, identification of various tissues and organs is required. In cardiac cross sectional images, distinguishing between myocardial tissue and blood is mandatory, and should preferably be performed automatically. Here, the problem of coarse detection of cardiac myocar...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 144 |
---|---|
container_issue | |
container_start_page | 141 |
container_title | |
container_volume | |
creator | Krips, R. Adam, D.R. |
description | When ultrasound images are examined, identification of various tissues and organs is required. In cardiac cross sectional images, distinguishing between myocardial tissue and blood is mandatory, and should preferably be performed automatically. Here, the problem of coarse detection of cardiac myocardial boundaries in short axis images is addressed using a segmentation method. Due to the complexity of this problem, time-space algorithms are used. Moreover, processing of an ultrasound image is based upon a multiresolution presentation of the image, using the discrete wavelet transform (DWT), which results in the segmentation of the image into blood and tissue areas. Heuristic time-space algorithms for improvement of the segmentation follow this stage. Comparison of the results to those of an expert cardiologist shows good agreement. |
doi_str_mv | 10.1109/CIC.1999.825926 |
format | Conference Proceeding |
fullrecord | <record><control><sourceid>proquest_6IE</sourceid><recordid>TN_cdi_ieee_primary_825926</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>825926</ieee_id><sourcerecordid>24857501</sourcerecordid><originalsourceid>FETCH-LOGICAL-i234t-272f9f86a11b0a89bfa71bd363cf59c21ad60db425f2b26778a2028adc7b4bd23</originalsourceid><addsrcrecordid>eNqFkE1LAzEYhAMqWGvPgqecvG1N3nwfZdFaWPCi5yXJJiWQ7tbNruK_t6XePQ3MPAzMIHRHyZpSYh7rbb2mxpi1BmFAXqAbojRhQlLOL9GCgJKVFFxdo1UpyREAcYyVWKBNE-KEv0I_jcnP2Y742-aMS9jtj56d0tDj1OM5T6Mtw9x32I9DKVUJ_pTZjNPe7kK5RVfR5hJWf7pEHy_P7_Vr1bxttvVTUyVgfKpAQTRRS0upI1YbF62irmOS-SiMB2o7STrHQURwIJXSFgho23nluOuALdHDufcwDp9zKFO7T8WHnG0fhrm0wLVQgtD_QcoFMHYC789gCiG0h_G4Z_xpz0eyX4OJZ24</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>21452331</pqid></control><display><type>conference_proceeding</type><title>Left ventricular wall segmentation in ultrasound cross-sectional images</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Krips, R. ; Adam, D.R.</creator><creatorcontrib>Krips, R. ; Adam, D.R.</creatorcontrib><description>When ultrasound images are examined, identification of various tissues and organs is required. In cardiac cross sectional images, distinguishing between myocardial tissue and blood is mandatory, and should preferably be performed automatically. Here, the problem of coarse detection of cardiac myocardial boundaries in short axis images is addressed using a segmentation method. Due to the complexity of this problem, time-space algorithms are used. Moreover, processing of an ultrasound image is based upon a multiresolution presentation of the image, using the discrete wavelet transform (DWT), which results in the segmentation of the image into blood and tissue areas. Heuristic time-space algorithms for improvement of the segmentation follow this stage. Comparison of the results to those of an expert cardiologist shows good agreement.</description><identifier>ISSN: 0276-6547</identifier><identifier>ISSN: 0276-6574</identifier><identifier>ISBN: 0780356144</identifier><identifier>ISBN: 9780780356146</identifier><identifier>DOI: 10.1109/CIC.1999.825926</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithms ; Blood ; Discrete wavelet transforms ; Image edge detection ; Image resolution ; Image segmentation ; Iterative algorithms ; Medical imaging ; Myocardium ; Sampling methods ; Speckle ; Tissue ; Ultrasonic imaging ; Wavelet transforms</subject><ispartof>Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004), 1999, p.141-144</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/825926$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,25140,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/825926$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Krips, R.</creatorcontrib><creatorcontrib>Adam, D.R.</creatorcontrib><title>Left ventricular wall segmentation in ultrasound cross-sectional images</title><title>Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004)</title><addtitle>CIC</addtitle><description>When ultrasound images are examined, identification of various tissues and organs is required. In cardiac cross sectional images, distinguishing between myocardial tissue and blood is mandatory, and should preferably be performed automatically. Here, the problem of coarse detection of cardiac myocardial boundaries in short axis images is addressed using a segmentation method. Due to the complexity of this problem, time-space algorithms are used. Moreover, processing of an ultrasound image is based upon a multiresolution presentation of the image, using the discrete wavelet transform (DWT), which results in the segmentation of the image into blood and tissue areas. Heuristic time-space algorithms for improvement of the segmentation follow this stage. Comparison of the results to those of an expert cardiologist shows good agreement.</description><subject>Algorithms</subject><subject>Blood</subject><subject>Discrete wavelet transforms</subject><subject>Image edge detection</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Iterative algorithms</subject><subject>Medical imaging</subject><subject>Myocardium</subject><subject>Sampling methods</subject><subject>Speckle</subject><subject>Tissue</subject><subject>Ultrasonic imaging</subject><subject>Wavelet transforms</subject><issn>0276-6547</issn><issn>0276-6574</issn><isbn>0780356144</isbn><isbn>9780780356146</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNqFkE1LAzEYhAMqWGvPgqecvG1N3nwfZdFaWPCi5yXJJiWQ7tbNruK_t6XePQ3MPAzMIHRHyZpSYh7rbb2mxpi1BmFAXqAbojRhQlLOL9GCgJKVFFxdo1UpyREAcYyVWKBNE-KEv0I_jcnP2Y742-aMS9jtj56d0tDj1OM5T6Mtw9x32I9DKVUJ_pTZjNPe7kK5RVfR5hJWf7pEHy_P7_Vr1bxttvVTUyVgfKpAQTRRS0upI1YbF62irmOS-SiMB2o7STrHQURwIJXSFgho23nluOuALdHDufcwDp9zKFO7T8WHnG0fhrm0wLVQgtD_QcoFMHYC789gCiG0h_G4Z_xpz0eyX4OJZ24</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Krips, R.</creator><creator>Adam, D.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>1999</creationdate><title>Left ventricular wall segmentation in ultrasound cross-sectional images</title><author>Krips, R. ; Adam, D.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i234t-272f9f86a11b0a89bfa71bd363cf59c21ad60db425f2b26778a2028adc7b4bd23</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Algorithms</topic><topic>Blood</topic><topic>Discrete wavelet transforms</topic><topic>Image edge detection</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Iterative algorithms</topic><topic>Medical imaging</topic><topic>Myocardium</topic><topic>Sampling methods</topic><topic>Speckle</topic><topic>Tissue</topic><topic>Ultrasonic imaging</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Krips, R.</creatorcontrib><creatorcontrib>Adam, D.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>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Krips, R.</au><au>Adam, D.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Left ventricular wall segmentation in ultrasound cross-sectional images</atitle><btitle>Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004)</btitle><stitle>CIC</stitle><date>1999</date><risdate>1999</risdate><spage>141</spage><epage>144</epage><pages>141-144</pages><issn>0276-6547</issn><issn>0276-6574</issn><isbn>0780356144</isbn><isbn>9780780356146</isbn><abstract>When ultrasound images are examined, identification of various tissues and organs is required. In cardiac cross sectional images, distinguishing between myocardial tissue and blood is mandatory, and should preferably be performed automatically. Here, the problem of coarse detection of cardiac myocardial boundaries in short axis images is addressed using a segmentation method. Due to the complexity of this problem, time-space algorithms are used. Moreover, processing of an ultrasound image is based upon a multiresolution presentation of the image, using the discrete wavelet transform (DWT), which results in the segmentation of the image into blood and tissue areas. Heuristic time-space algorithms for improvement of the segmentation follow this stage. Comparison of the results to those of an expert cardiologist shows good agreement.</abstract><pub>IEEE</pub><doi>10.1109/CIC.1999.825926</doi><tpages>4</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 0276-6547 |
ispartof | Computers in Cardiology 1999. Vol.26 (Cat. No.99CH37004), 1999, p.141-144 |
issn | 0276-6547 0276-6574 |
language | eng |
recordid | cdi_ieee_primary_825926 |
source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Algorithms Blood Discrete wavelet transforms Image edge detection Image resolution Image segmentation Iterative algorithms Medical imaging Myocardium Sampling methods Speckle Tissue Ultrasonic imaging Wavelet transforms |
title | Left ventricular wall segmentation in ultrasound cross-sectional images |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T12%3A28%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Left%20ventricular%20wall%20segmentation%20in%20ultrasound%20cross-sectional%20images&rft.btitle=Computers%20in%20Cardiology%201999.%20Vol.26%20(Cat.%20No.99CH37004)&rft.au=Krips,%20R.&rft.date=1999&rft.spage=141&rft.epage=144&rft.pages=141-144&rft.issn=0276-6547&rft.isbn=0780356144&rft.isbn_list=9780780356146&rft_id=info:doi/10.1109/CIC.1999.825926&rft_dat=%3Cproquest_6IE%3E24857501%3C/proquest_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=21452331&rft_id=info:pmid/&rft_ieee_id=825926&rfr_iscdi=true |