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Neural networks aided stone detection in thick slab MRCP images
This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D...
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Published in: | Medical & biological engineering & computing 2006-08, Vol.44 (8), p.711-719 |
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description | This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver. |
doi_str_mv | 10.1007/s11517-006-0083-8 |
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The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-006-0083-8</identifier><identifier>PMID: 16937213</identifier><language>eng</language><publisher>United States: Springer Nature B.V</publisher><subject>Algorithms ; Biliary Tract - diagnostic imaging ; Cholangiopancreatography, Magnetic Resonance - methods ; Choledocholithiasis - diagnostic imaging ; Cholelithiasis - diagnostic imaging ; Gallstones - diagnostic imaging ; Humans ; Neural networks ; Neural Networks (Computer) ; Radiographic Image Enhancement - methods</subject><ispartof>Medical & biological engineering & computing, 2006-08, Vol.44 (8), p.711-719</ispartof><rights>International Federation for Medical and Biological Engineering 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-e1e90ca4f2ff00dafa4bfab61b95582e5e3efde3fd028a3ea34e1e87aabe9dc63</citedby><cites>FETCH-LOGICAL-c357t-e1e90ca4f2ff00dafa4bfab61b95582e5e3efde3fd028a3ea34e1e87aabe9dc63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/661222968/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/661222968?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11668,27903,27904,36039,36040,44342,74641</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16937213$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Logeswaran, Rajasvaran</creatorcontrib><title>Neural networks aided stone detection in thick slab MRCP images</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><description>This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. 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The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.</description><subject>Algorithms</subject><subject>Biliary Tract - diagnostic imaging</subject><subject>Cholangiopancreatography, Magnetic Resonance - methods</subject><subject>Choledocholithiasis - diagnostic imaging</subject><subject>Cholelithiasis - diagnostic imaging</subject><subject>Gallstones - diagnostic imaging</subject><subject>Humans</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Radiographic Image Enhancement - methods</subject><issn>0140-0118</issn><issn>1741-0444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNqFkU1Lw0AQhhdRbK3-AC-yePAW3dmvbE4ixS-oH4iel00yq2nTpO4miP_elBYELx6GuTzvCzMPIcfAzoGx9CICKEgTxvQwRiRmh4whlZAwKeUuGTOQLGEAZkQOYpwzxkFxuU9GoDORchBjcvmIfXA1bbD7asMiUleVWNLYtQ3SEjssuqptaNXQ7qMqFjTWLqcPL9NnWi3dO8ZDsuddHfFouyfk7eb6dXqXzJ5u76dXs6QQKu0SBMxY4aTn3jNWOu9k7l2uIc-UMhwVCvQlCl8ybpxAJ-QQMalzOWZlocWEnG16V6H97DF2dlnFAuvaNdj20WqTGqXE_yBkKpVSqwE8_QPO2z40wxFWa-CcZ9oMEGygIrQxBvR2FYbDw7cFZtcO7MaBHRzYtQO7zpxsi_t8ieVvYvt08QPzloGZ</recordid><startdate>200608</startdate><enddate>200608</enddate><creator>Logeswaran, Rajasvaran</creator><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7SC</scope><scope>7TB</scope><scope>7TS</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>L.-</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7QO</scope><scope>7X8</scope></search><sort><creationdate>200608</creationdate><title>Neural networks aided stone detection in thick slab MRCP images</title><author>Logeswaran, Rajasvaran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-e1e90ca4f2ff00dafa4bfab61b95582e5e3efde3fd028a3ea34e1e87aabe9dc63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Biliary Tract - 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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>Logeswaran, Rajasvaran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural networks aided stone detection in thick slab MRCP images</atitle><jtitle>Medical & biological engineering & computing</jtitle><addtitle>Med Biol Eng Comput</addtitle><date>2006-08</date><risdate>2006</risdate><volume>44</volume><issue>8</issue><spage>711</spage><epage>719</epage><pages>711-719</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.</abstract><cop>United States</cop><pub>Springer Nature B.V</pub><pmid>16937213</pmid><doi>10.1007/s11517-006-0083-8</doi><tpages>9</tpages></addata></record> |
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subjects | Algorithms Biliary Tract - diagnostic imaging Cholangiopancreatography, Magnetic Resonance - methods Choledocholithiasis - diagnostic imaging Cholelithiasis - diagnostic imaging Gallstones - diagnostic imaging Humans Neural networks Neural Networks (Computer) Radiographic Image Enhancement - methods |
title | Neural networks aided stone detection in thick slab MRCP images |
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