A novel method for retinal vessel tracking using particle filters
Abstract Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use...
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description | Abstract Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels. |
doi_str_mv | 10.1016/j.compbiomed.2013.01.016 |
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In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2013.01.016</identifier><identifier>PMID: 23434235</identifier><identifier>CODEN: CBMDAW</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Atoms & subatomic particles ; Bifurcations ; Blood vessels ; Databases, Factual ; Diabetic retinopathy ; Diagnostic Techniques, Ophthalmological ; Eyes & eyesight ; Humans ; Image Processing, Computer-Assisted - methods ; Internal Medicine ; Kalman filters ; Methods ; Monte Carlo Method ; Other ; Particle filtering ; Propagation ; Retina - anatomy & histology ; Retinal image processing ; Retinal Vessels - anatomy & histology ; Studies ; Tracking</subject><ispartof>Computers in biology and medicine, 2013-06, Vol.43 (5), p.541-548</ispartof><rights>2013</rights><rights>Copyright © 2013. Published by Elsevier Ltd.</rights><rights>Copyright Elsevier Limited Jun 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c531t-81826c3e7e63fcb4ac552f823c6ab1d3a720206e666296594c7e6f82c696a89f3</citedby><cites>FETCH-LOGICAL-c531t-81826c3e7e63fcb4ac552f823c6ab1d3a720206e666296594c7e6f82c696a89f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1324273670?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23434235$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nayebifar, B</creatorcontrib><creatorcontrib>Abrishami Moghaddam, H</creatorcontrib><title>A novel method for retinal vessel tracking using particle filters</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Abstract Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels.</description><subject>Algorithms</subject><subject>Atoms & subatomic particles</subject><subject>Bifurcations</subject><subject>Blood vessels</subject><subject>Databases, Factual</subject><subject>Diabetic retinopathy</subject><subject>Diagnostic Techniques, Ophthalmological</subject><subject>Eyes & eyesight</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Internal Medicine</subject><subject>Kalman filters</subject><subject>Methods</subject><subject>Monte Carlo Method</subject><subject>Other</subject><subject>Particle filtering</subject><subject>Propagation</subject><subject>Retina - anatomy & histology</subject><subject>Retinal image processing</subject><subject>Retinal Vessels - anatomy & histology</subject><subject>Studies</subject><subject>Tracking</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkkFr3DAQhUVpaTZp_0Ix9NKLtyONJduXwja0TSGQQxPoTWjlcauNbG0leyH_vnI3IZBLA4N0mE_zGL3HWMFhzYGrj7u1DcN-68JA3VoAxzXwXOoFW_GmbkuQWL1kKwAOZdUIecJOU9oBQAUIr9mJwAorgXLFNptiDAfyxUDT79AVfYhFpMmNxhcHSil3pmjsrRt_FXNazr2Jk7Oeit75iWJ6w171xid6e3-fsZuvX67PL8rLq2_fzzeXpZXIp7LhjVAWqSaFvd1Wxkop-kagVWbLOzS1AAGKlFKiVbKtbCZz36pWmabt8Yx9OM7dx_BnpjTpwSVL3puRwpw0zysBylry_6MoJCJvpcro-yfoLswxb_-PqkSNqoZMNUfKxpBSpF7voxtMvNMc9OKI3ulHR_TiiAaeaxF4dy8wb5few8MHCzLw-QhQ_ryDo6iTdTRa6lwkO-kuuOeofHoyxHo3Omv8Ld1RetxJJ6FB_1iSsQSDYw4Fqp_4F1X2tBU</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Nayebifar, B</creator><creator>Abrishami Moghaddam, H</creator><general>Elsevier Ltd</general><general>Elsevier Limited</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>7X7</scope><scope>7XB</scope><scope>88E</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>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>7QO</scope></search><sort><creationdate>20130601</creationdate><title>A novel method for retinal vessel tracking using particle filters</title><author>Nayebifar, B ; Abrishami Moghaddam, H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c531t-81826c3e7e63fcb4ac552f823c6ab1d3a720206e666296594c7e6f82c696a89f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Atoms & subatomic particles</topic><topic>Bifurcations</topic><topic>Blood vessels</topic><topic>Databases, Factual</topic><topic>Diabetic retinopathy</topic><topic>Diagnostic Techniques, Ophthalmological</topic><topic>Eyes & eyesight</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Internal Medicine</topic><topic>Kalman filters</topic><topic>Methods</topic><topic>Monte Carlo Method</topic><topic>Other</topic><topic>Particle filtering</topic><topic>Propagation</topic><topic>Retina - anatomy & histology</topic><topic>Retinal image processing</topic><topic>Retinal Vessels - anatomy & histology</topic><topic>Studies</topic><topic>Tracking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nayebifar, B</creatorcontrib><creatorcontrib>Abrishami Moghaddam, H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>Proquest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nayebifar, B</au><au>Abrishami Moghaddam, H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel method for retinal vessel tracking using particle filters</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2013-06-01</date><risdate>2013</risdate><volume>43</volume><issue>5</issue><spage>541</spage><epage>548</epage><pages>541-548</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><coden>CBMDAW</coden><abstract>Abstract Extraction of a proper map from the vessel paths in the retinal images is a prerequisite for many applications such as identification. In this paper, we present a new approach based on particle filtering to determine and locally track the vessel paths in retina. Particle filter needs to use an acceptable probability density function (PDF) describing the blood vessels which must be provided by the retinal image. For this purpose, the product of the green and blue channels of the RGB retinal images is considered and after a median filtering stage, it is used as a PDF for tracking procedure. Then a stage of optic disc localization is performed to localize the starting points around the optic disc. With a proper set of starting points, the iterative tracking procedure initiates. First, a uniform propagation of the particles on an annular ring around each point (including starting points or ones determined as central points in the previous iteration) is performed. The particle weights are evaluated and accordingly, each particle is decided to be inside or outside the vessel. The subsequent stage is to analyze the hypothetical vectors between a central point and each of the inside vessel particles to find ones located inside vessel. Afterwards, the particles are clustered using quality threshold clustering method. Finally, each cluster introduces a central point for pursuing the tracking procedure in the next iteration. The tracking proceeds towards a bifurcation or the end of the vessels. We introduced two criteria: automatic/manually tracked ratio (AMTR) and false/manually tracked ratio (FMTR) for evaluating the tracking results. Apart from the labeling accuracy, the average values of AMTR and FMTR were 0.7746 and 0.2091, respectively. The proposed method successfully deals with the bifurcations with robustness against noise and tracks the thin vessels.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>23434235</pmid><doi>10.1016/j.compbiomed.2013.01.016</doi><tpages>8</tpages></addata></record> |
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subjects | Algorithms Atoms & subatomic particles Bifurcations Blood vessels Databases, Factual Diabetic retinopathy Diagnostic Techniques, Ophthalmological Eyes & eyesight Humans Image Processing, Computer-Assisted - methods Internal Medicine Kalman filters Methods Monte Carlo Method Other Particle filtering Propagation Retina - anatomy & histology Retinal image processing Retinal Vessels - anatomy & histology Studies Tracking |
title | A novel method for retinal vessel tracking using particle filters |
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