Artificial neural network: border detection in echocardiography
Being non-invasive and low cost, the echocardiography has become a diagnostic technique largely applied for the determination of the left ventricle systolic and diastolic volumes, which are used indirectly to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, th...
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| Veröffentlicht in: | Medical & biological engineering & computing 2008-09, Vol.46 (9), p.841-848, Article 841 |
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| creator | Wu, Eduardo Jyh Herng De Andrade, Márcio Luiz Nicolosi, Denys E. Pontes, Sérgio C. |
| description | Being non-invasive and low cost, the echocardiography has become a diagnostic technique largely applied for the determination of the left ventricle systolic and diastolic volumes, which are used indirectly to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional ejection fraction, the myocardial thickness, and the ventricular mass, etc. However, the image is very noisy, which renders the delineation of the borders of the left ventricle very difficult. While there are many techniques image segmentation, this work chooses the artificial neural network (ANN) since it is not very sensitive to noise. In order to reduce the processing time, the operator selects the region of interest where the neural network will identify the borders. Neighborhood and gradient search techniques are then employed to link the points and the left ventricle contour is traced. The present method has been efficient in detecting the left ventricle borders echocardiography images compared to those whose borders were delineated by the specialists. For good results, it is important to choose properly the areas to be analyzed and the central points of these areas. |
| doi_str_mv | 10.1007/s11517-008-0372-5 |
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While there are many techniques image segmentation, this work chooses the artificial neural network (ANN) since it is not very sensitive to noise. In order to reduce the processing time, the operator selects the region of interest where the neural network will identify the borders. Neighborhood and gradient search techniques are then employed to link the points and the left ventricle contour is traced. The present method has been efficient in detecting the left ventricle borders echocardiography images compared to those whose borders were delineated by the specialists. For good results, it is important to choose properly the areas to be analyzed and the central points of these areas.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-008-0372-5</identifier><identifier>PMID: 18626675</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Algorithms ; Automation ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Cardiology ; Computer Applications ; Diagnostics ; Echocardiography - methods ; Ejection fraction ; Heart Ventricles - diagnostic imaging ; Human Physiology ; Humans ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Neural networks ; Neural Networks (Computer) ; Neurons ; Radiology ; Review Article ; Studies</subject><ispartof>Medical & biological engineering & computing, 2008-09, Vol.46 (9), p.841-848, Article 841</ispartof><rights>International Federation for Medical and Biological Engineering 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c431t-c7843a35131bef6b22cc7ac7d4eb689eba93159d2e004e15b3f41f19a00a50c13</citedby><cites>FETCH-LOGICAL-c431t-c7843a35131bef6b22cc7ac7d4eb689eba93159d2e004e15b3f41f19a00a50c13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-008-0372-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-008-0372-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18626675$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Eduardo Jyh Herng</creatorcontrib><creatorcontrib>De Andrade, Márcio Luiz</creatorcontrib><creatorcontrib>Nicolosi, Denys E.</creatorcontrib><creatorcontrib>Pontes, Sérgio C.</creatorcontrib><title>Artificial neural network: border detection in echocardiography</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>Being non-invasive and low cost, the echocardiography has become a diagnostic technique largely applied for the determination of the left ventricle systolic and diastolic volumes, which are used indirectly to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional ejection fraction, the myocardial thickness, and the ventricular mass, etc. However, the image is very noisy, which renders the delineation of the borders of the left ventricle very difficult. While there are many techniques image segmentation, this work chooses the artificial neural network (ANN) since it is not very sensitive to noise. In order to reduce the processing time, the operator selects the region of interest where the neural network will identify the borders. Neighborhood and gradient search techniques are then employed to link the points and the left ventricle contour is traced. The present method has been efficient in detecting the left ventricle borders echocardiography images compared to those whose borders were delineated by the specialists. 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Academic</collection><jtitle>Medical & biological engineering & computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Eduardo Jyh Herng</au><au>De Andrade, Márcio Luiz</au><au>Nicolosi, Denys E.</au><au>Pontes, Sérgio C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural network: border detection in echocardiography</atitle><jtitle>Medical & biological engineering & computing</jtitle><stitle>Med Biol Eng Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2008-09-01</date><risdate>2008</risdate><volume>46</volume><issue>9</issue><spage>841</spage><epage>848</epage><pages>841-848</pages><artnum>841</artnum><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>Being non-invasive and low cost, the echocardiography has become a diagnostic technique largely applied for the determination of the left ventricle systolic and diastolic volumes, which are used indirectly to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional ejection fraction, the myocardial thickness, and the ventricular mass, etc. However, the image is very noisy, which renders the delineation of the borders of the left ventricle very difficult. While there are many techniques image segmentation, this work chooses the artificial neural network (ANN) since it is not very sensitive to noise. In order to reduce the processing time, the operator selects the region of interest where the neural network will identify the borders. Neighborhood and gradient search techniques are then employed to link the points and the left ventricle contour is traced. The present method has been efficient in detecting the left ventricle borders echocardiography images compared to those whose borders were delineated by the specialists. For good results, it is important to choose properly the areas to be analyzed and the central points of these areas.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><pmid>18626675</pmid><doi>10.1007/s11517-008-0372-5</doi><tpages>8</tpages></addata></record> |
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| subjects | Algorithms Automation Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Cardiology Computer Applications Diagnostics Echocardiography - methods Ejection fraction Heart Ventricles - diagnostic imaging Human Physiology Humans Image Interpretation, Computer-Assisted - methods Imaging Neural networks Neural Networks (Computer) Neurons Radiology Review Article Studies |
| title | Artificial neural network: border detection in echocardiography |
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