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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/1/132 |
| _version_ | 1797543396123869184 |
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| author | Xi Li Zhangyong Li Dewei Yang Lisha Zhong Lian Huang Jinzhao Lin |
| author_facet | Xi Li Zhangyong Li Dewei Yang Lisha Zhong Lian Huang Jinzhao Lin |
| author_sort | Xi Li |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-10T13:44:00Z |
| format | Article |
| id | doaj.art-d9538dbaa8fd498885f7b71e10c1820a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:44:00Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d9538dbaa8fd498885f7b71e10c1820a2023-11-21T02:47:29ZengMDPI AGSensors1424-82202020-12-0121113210.3390/s21010132Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood CollectionXi Li0Zhangyong Li1Dewei Yang2Lisha Zhong3Lian Huang4Jinzhao Lin5School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaIn 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%.https://www.mdpi.com/1424-8220/21/1/132finger veinGaborGaussian mixture modelimage segmentation |
| spellingShingle | Xi Li Zhangyong Li Dewei Yang Lisha Zhong Lian Huang Jinzhao Lin Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection Sensors finger vein Gabor Gaussian mixture model image segmentation |
| title | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
| title_full | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
| title_fullStr | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
| title_full_unstemmed | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
| title_short | Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection |
| title_sort | research on finger vein image segmentation and blood sampling point location in automatic blood collection |
| topic | finger vein Gabor Gaussian mixture model image segmentation |
| url | https://www.mdpi.com/1424-8220/21/1/132 |
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