A novel algorithm of dorsal hand vein image segmentation by integrating matched filter and local binary fitting level set model

Guo, Ziyang; Ma, Yao; Min, Xiaolin; Li, Hui; Liu, Qingyi; Han, Chao; Yang, Guang; Bai, Peirui; Ren, Yande

doi:10.1109/icisce50968.2020.00027

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Hence, different image segmentation techniques have been implemented with varying degrees of success. These techniques include manual cropping with a combined matched filter and a local binary fitting model to locate tiny boundaries (small veins) in images [15]. The image centroid technique is also used for the segmentation of DHVs [16].…”

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confidence: 99%

Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

et al. 2023

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Objectives: 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-of-interest (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.

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confidence: 99%