Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection
In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segment...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2020-12, Vol.21 (1), p.132, Article 132 |
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| creator | Li, Xi Li, Zhangyong Yang, Dewei Zhong, Lisha Huang, Lian Lin, Jinzhao |
| description | In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%. |
| doi_str_mv | 10.3390/s21010132 |
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In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s21010132</identifier><identifier>PMID: 33379213</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>Accuracy ; Algorithms ; Blood ; Chemistry ; Chemistry, Analytical ; Corner detection ; Datasets ; Deep learning ; Engineering ; Engineering, Electrical & Electronic ; Entropy ; finger vein ; Fuzzy sets ; Gabor ; Gabor filters ; Gabor transformation ; Gaussian mixture model ; Hematologic Tests ; Image Processing, Computer-Assisted ; image segmentation ; Instruments & Instrumentation ; Letter ; Methods ; Neural networks ; Normal Distribution ; Physical Sciences ; Sampling ; Science & Technology ; Support vector machines ; Technology ; Veins ; Veins & arteries ; Veins - diagnostic imaging</subject><ispartof>Sensors (Basel, Switzerland), 2020-12, Vol.21 (1), p.132, Article 132</ispartof><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 by the authors. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>2</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000606278400001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c469t-4281f35d23b0a86f1c4d59d47b4faa808f6e74dea75ce21c177f6272084d67d33</citedby><cites>FETCH-LOGICAL-c469t-4281f35d23b0a86f1c4d59d47b4faa808f6e74dea75ce21c177f6272084d67d33</cites><orcidid>0000-0002-3914-4445 ; 0000-0003-4157-9066 ; 0000-0002-3918-069X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795357/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795357/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2104,2116,27931,27932,39265,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33379213$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xi</creatorcontrib><creatorcontrib>Li, Zhangyong</creatorcontrib><creatorcontrib>Yang, Dewei</creatorcontrib><creatorcontrib>Zhong, Lisha</creatorcontrib><creatorcontrib>Huang, Lian</creatorcontrib><creatorcontrib>Lin, Jinzhao</creatorcontrib><title>Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection</title><title>Sensors (Basel, Switzerland)</title><addtitle>SENSORS-BASEL</addtitle><addtitle>Sensors (Basel)</addtitle><description>In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Blood</subject><subject>Chemistry</subject><subject>Chemistry, Analytical</subject><subject>Corner detection</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Entropy</subject><subject>finger vein</subject><subject>Fuzzy sets</subject><subject>Gabor</subject><subject>Gabor filters</subject><subject>Gabor transformation</subject><subject>Gaussian mixture model</subject><subject>Hematologic Tests</subject><subject>Image Processing, Computer-Assisted</subject><subject>image segmentation</subject><subject>Instruments & Instrumentation</subject><subject>Letter</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Normal Distribution</subject><subject>Physical Sciences</subject><subject>Sampling</subject><subject>Science & Technology</subject><subject>Support vector machines</subject><subject>Technology</subject><subject>Veins</subject><subject>Veins & arteries</subject><subject>Veins - diagnostic imaging</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkluL1DAUgIso7kUf_ANS8MVFRnNrk74Ia3F1YEBx1deQJifdDG0yJq3ivzezHYddnyQPuZwvXw4npyieYfSa0ga9SQSjPCh5UJxiRthKEIIe3lmfFGcpbREilFLxuDjJE28IpqfF9gskUFHflMGXV873EMvv4Hy5HlUP5TX0I_hJTS6HlTfluyEEU16rcTdkuPwcnJ_KTdALke9dzlMY804f0DYMA-h99EnxyKohwdPDfF58u3r_tf242nz6sG4vNyvN6mZaMSKwpZUhtENK1BZrZqrGMN4xq5RAwtbAmQHFKw0Ea8y5rQknSDBTc0PpebFevCaordxFN6r4Wwbl5O1BiL1UMSc4gDRNRYXpstYa1gghKss7jgEjjQVBKrveLq7d3I1gdK5FVMM96f2IdzeyDz8l51ld8Sx4eRDE8GOGNMnRJQ3DoDyEOUnCOGNN3Yg9-uIfdBvm6HOpbqmKYMJwpi4WSseQUgR7TAYjue8GeeyGzD6_m_2R_Pv9GXi1AL-gCzZpB17DEUMI1SiXVrC8Qvunxf_TrVu6pg2zn-gf9i_PZg</recordid><startdate>20201228</startdate><enddate>20201228</enddate><creator>Li, Xi</creator><creator>Li, Zhangyong</creator><creator>Yang, Dewei</creator><creator>Zhong, Lisha</creator><creator>Huang, Lian</creator><creator>Lin, Jinzhao</creator><general>Mdpi</general><general>MDPI AG</general><general>MDPI</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3914-4445</orcidid><orcidid>https://orcid.org/0000-0003-4157-9066</orcidid><orcidid>https://orcid.org/0000-0002-3918-069X</orcidid></search><sort><creationdate>20201228</creationdate><title>Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection</title><author>Li, Xi ; 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In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.</abstract><cop>BASEL</cop><pub>Mdpi</pub><pmid>33379213</pmid><doi>10.3390/s21010132</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-3914-4445</orcidid><orcidid>https://orcid.org/0000-0003-4157-9066</orcidid><orcidid>https://orcid.org/0000-0002-3918-069X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Blood Chemistry Chemistry, Analytical Corner detection Datasets Deep learning Engineering Engineering, Electrical & Electronic Entropy finger vein Fuzzy sets Gabor Gabor filters Gabor transformation Gaussian mixture model Hematologic Tests Image Processing, Computer-Assisted image segmentation Instruments & Instrumentation Letter Methods Neural networks Normal Distribution Physical Sciences Sampling Science & Technology Support vector machines Technology Veins Veins & arteries Veins - diagnostic imaging |
| title | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
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