Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods
Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. Methods: Manual se...
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| Format: | Article |
| Language: | English |
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HIR
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10375/1/J16048_96765c3676a37ea9fa555805a2d79d35.pdf |
| _version_ | 1796870155559501824 |
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| author | Waheed Ali Laghari, Waheed Ali Laghari Audrey Huong, Audrey Huong Kim Gaik Tay, Kim Gaik Tay Chang Choon Chew, Chang Choon Chew |
| author_facet | Waheed Ali Laghari, Waheed Ali Laghari Audrey Huong, Audrey Huong Kim Gaik Tay, Kim Gaik Tay Chang Choon Chew, Chang Choon Chew |
| author_sort | Waheed Ali Laghari, Waheed Ali Laghari |
| collection | UTHM |
| description | Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets
with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. Methods: Manual segmentation involved selecting a region-ofinterest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset. Results: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique. Conclusions: Our technique can be feasible for extracting the ROI in DHV images. This strategy
provides higher consistency and greater efficiency than the manual approach. |
| first_indexed | 2024-03-05T22:05:21Z |
| format | Article |
| id | uthm.eprints-10375 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:05:21Z |
| publishDate | 2023 |
| publisher | HIR |
| record_format | dspace |
| spelling | uthm.eprints-103752023-11-21T01:11:10Z http://eprints.uthm.edu.my/10375/ Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods Waheed Ali Laghari, Waheed Ali Laghari Audrey Huong, Audrey Huong Kim Gaik Tay, Kim Gaik Tay Chang Choon Chew, Chang Choon Chew T Technology (General) Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. Methods: Manual segmentation involved selecting a region-ofinterest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset. Results: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique. Conclusions: Our technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach. HIR 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10375/1/J16048_96765c3676a37ea9fa555805a2d79d35.pdf Waheed Ali Laghari, Waheed Ali Laghari and Audrey Huong, Audrey Huong and Kim Gaik Tay, Kim Gaik Tay and Chang Choon Chew, Chang Choon Chew (2023) Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods. Dorsal Hand Vein Pattern Recognition, 29 (2). pp. 1-9. ISSN 2093-3681 https://doi.org/10.4258/hir.2023.29.2.152 |
| spellingShingle | T Technology (General) Waheed Ali Laghari, Waheed Ali Laghari Audrey Huong, Audrey Huong Kim Gaik Tay, Kim Gaik Tay Chang Choon Chew, Chang Choon Chew Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title | Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title_full | Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title_fullStr | Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title_full_unstemmed | Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title_short | Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods |
| title_sort | dorsal hand vein pattern recognition a comparison between manual and automatic segmentation methods |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10375/1/J16048_96765c3676a37ea9fa555805a2d79d35.pdf |
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