Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model
Difficulty in achieving intravenous access in some patients is a clinical problem due to extreme age, body size, and chronic disease patients. In biometric identification, hand vein patterns are useful when other external identifiers are more prone to be damaged or forged. To overcome these problems...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39631/ http://umpir.ump.edu.my/id/eprint/39631/1/Dorsal%20Hand%20Vein%20Segmentation%20Using%20Vein-Generative%20Adversarial.pdf http://umpir.ump.edu.my/id/eprint/39631/2/Dorsal%20hand%20vein%20segmentation%20using%20Vein-Generative%20Adversarial%20Network%20%28V-GAN%29%20Model_ABS.pdf |
| _version_ | 1848825820067397632 |
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| author | Marlina, Yakno Junita, Mohamad-Saleh Mohd Zamri, Ibrahim |
| author_facet | Marlina, Yakno Junita, Mohamad-Saleh Mohd Zamri, Ibrahim |
| author_sort | Marlina, Yakno |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Difficulty in achieving intravenous access in some patients is a clinical problem due to extreme age, body size, and chronic disease patients. In biometric identification, hand vein patterns are useful when other external identifiers are more prone to be damaged or forged. To overcome these problems, near-infrared dorsal hand vein images are captured and segmented for vein extraction. However, the segmentation process becomes more challenging when the infrared im- ages suffer from extremely low contrast and distortion, indirectly affecting the segmentation process. Therefore, this work presents a method for generating an accurate map of dorsal hand vein patterns using deep learning Vein-Generative Adversarial Networks (V-GAN). The performance of V-GAN is measured in terms of accuracy, Area under Curve (AUC), F1-score, sensitivity, specificity, and dice-coefficient. |
| first_indexed | 2025-11-15T03:35:00Z |
| format | Conference or Workshop Item |
| id | ump-39631 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:35:00Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-396312023-12-13T03:34:03Z http://umpir.ump.edu.my/id/eprint/39631/ Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model Marlina, Yakno Junita, Mohamad-Saleh Mohd Zamri, Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Difficulty in achieving intravenous access in some patients is a clinical problem due to extreme age, body size, and chronic disease patients. In biometric identification, hand vein patterns are useful when other external identifiers are more prone to be damaged or forged. To overcome these problems, near-infrared dorsal hand vein images are captured and segmented for vein extraction. However, the segmentation process becomes more challenging when the infrared im- ages suffer from extremely low contrast and distortion, indirectly affecting the segmentation process. Therefore, this work presents a method for generating an accurate map of dorsal hand vein patterns using deep learning Vein-Generative Adversarial Networks (V-GAN). The performance of V-GAN is measured in terms of accuracy, Area under Curve (AUC), F1-score, sensitivity, specificity, and dice-coefficient. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39631/1/Dorsal%20Hand%20Vein%20Segmentation%20Using%20Vein-Generative%20Adversarial.pdf pdf en http://umpir.ump.edu.my/id/eprint/39631/2/Dorsal%20hand%20vein%20segmentation%20using%20Vein-Generative%20Adversarial%20Network%20%28V-GAN%29%20Model_ABS.pdf Marlina, Yakno and Junita, Mohamad-Saleh and Mohd Zamri, Ibrahim (2022) Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model. In: Lecture Notes in Electrical Engineering; 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 , 5-6 April 2021 , Virtual, Online. pp. 585-591., 829 LNEE (272139). ISSN 1876-1100 ISBN 978-981168128-8 (Published) https://doi.org/10.1007/978-981-16-8129-5_89 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Marlina, Yakno Junita, Mohamad-Saleh Mohd Zamri, Ibrahim Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title | Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title_full | Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title_fullStr | Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title_full_unstemmed | Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title_short | Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model |
| title_sort | dorsal hand vein segmentation using vein-generative adversarial network (v-gan) model |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39631/ http://umpir.ump.edu.my/id/eprint/39631/ http://umpir.ump.edu.my/id/eprint/39631/1/Dorsal%20Hand%20Vein%20Segmentation%20Using%20Vein-Generative%20Adversarial.pdf http://umpir.ump.edu.my/id/eprint/39631/2/Dorsal%20hand%20vein%20segmentation%20using%20Vein-Generative%20Adversarial%20Network%20%28V-GAN%29%20Model_ABS.pdf |