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

Abstract

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.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning

“…We employed the modified AlexNet algorithm 32 (refer to supplementary material B) with a training batch size of 8 incorporating a decay rate of 1e-4 and a learning rate of 1e-3. The loss function used was categorical cross-entropy, and the optimization was carried out using the Adam optimizer.…”

“…38 , Inception-v439 , AlexNet32 , Squeeze-and-Excitation Network40 , ResNet50 41 , and DenseNet 42 , was conducted. To ensure a fair comparison, consistent training conditions were maintained with 100 epochs, a learning rate of 1e-3, and the use of the Adam optimizer.…”