Search Results (15)

Dorsal hand vein recognition, with unique stable and reliable advantages, has attracted considerable attention from numerous researchers. In this case, the dorsal hand vein images captured by the means of transmission infrared imaging are clearer than those collected by other infrared methods, enabling it to be more suitable for the biometric applications. However, less attention is paid to individual age estimation based on dorsal hand veins. To this end, this paper proposes an efficient dorsal hand vein age estimation model using a deep neural network with attention mechanisms. Specifically, a convolutional neural network (CNN) is developed to extract the expressive features for age estimation. Simultaneously, another deep residual network is leveraged to strengthen the representation ability on subtle dorsal vein textures. Moreover, variable activation functions and pooling layers are integrated into the respective streams to enhance the nonlinearity modeling of the dual-stream model. Finally, a dynamic attention mechanism module is embedded into the dual-stream network to achieve multi-modal collaborative enhancement, guiding the model to concentrate on salient age-specific features. To evaluate the performance of dorsal hand vein age estimation, this work collects dorsal hand vein images using the transmission near-infrared spectrum from 300 individuals across various age groups. The experimental results show that the dual-stream enhanced network with the attention mechanism significantly improves the accuracy of dorsal hand vein age estimation in comparison with other deep learning approaches, indicating the potential of using near-infrared dorsal hand vein imaging and deep learning technology for efficient human age estimation. Full article

Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a coaxial correction for image alignment in the self-built vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by means of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combines coaxial correction and a homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, we used a hot mirror to adjust the light paths of both the projector and the camera to be coaxial, and then aligned the projected image with the dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection-over-union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in 0.09 s, and the largest distance error of vein projection onto the dorsal hand is 0.53 mm. The experiments show that the device has reached practical accuracy and has values of research and application. Full article

In response to the difficulty of traditional image processing methods to quickly and accurately extract regions of interest from non-contact dorsal hand vein images in complex backgrounds, this study proposes a model based on an improved U-Net for dorsal hand keypoint detection. The residual module was added to the downsampling path of the U-Net network to solve the model degradation problem and improve the feature information extraction ability of the network; the Jensen–Shannon (JS) divergence loss function was used to supervise the final feature map distribution so that the output feature map tended to Gaussian distribution and improved the feature map multi-peak problem; and Soft-argmax is used to calculate the keypoint coordinates of the final feature map to realize end-to-end training. The experimental results showed that the accuracy of the improved U-Net network model reached 98.6%, which was 1% better than the original U-Net network model; the improved U-Net network model file was only 1.16 M, which achieved a higher accuracy than the original U-Net network model with significantly reduced model parameters. Therefore, the improved U-Net model in this study can realize dorsal hand keypoint detection (for region of interest extraction) for non-contact dorsal hand vein images and is suitable for practical deployment in low-resource platforms such as edge-embedded systems. Full article

Rabbits are commonly used for pharmacokinetic (PK) and toxicokinetic (TK) studies in the research setting, requiring repetitive venipuncture, which can be challenging in this species. The auricular vessels are commonly used for venipuncture in rabbits. The repetitive access of these delicate vessels can lead to trauma such as hematomas causing venipuncture to become more challenging as the study progresses. In turn, this leads to missed time points or insufficient blood samples. Surgical models for chronic vascular access in rabbits are common throughout the industry. Common models include exteriorized vascular catheters and implanted vascular access ports. However, these implants come with their own complications and restrictions when used in rabbits. Therefore, the authors evaluated the use of a vascular access button (VAB), an implant commonly used in small rodents, as a refinement to the current chronic models in use in the industry. Seventeen rabbits were implanted with either single or dual channel VABs. The catheters were implanted in the femoral artery and/or vein and then tunneled subcutaneously to the button on the dorsal thoracic area. Overall, the results were outstanding, and an established model was created. The average patency rate was 316 days with several implants still patent after 2 years. The authors feel the implantation and use of a vascular access button in rabbits for routine PK studies is an excellent refinement. The rabbits tolerate the buttons extremely well with minimal issues. The patency rate is equal to or better than vascular access ports and when used with the tethering system, provides a hands-off method for blood collection and intravenous administration in rabbits during PK studies. Full article

As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have few images. To solve the above problems, we propose a method based on the fusion of ResNet and Histograms of Oriented Gradients (HOG) features, in which the shallow semantic information extracted by primary convolution and HOG features are fed into the residual structure of ResNet for full fusion and, finally, classification. By adding Gaussian noise, the North China University of Technology dataset, the Shandong University of Science and Technology dataset, and the Eastern Mediterranean University dataset are extended and fused to from a fused dataset. Our proposed method is applied to the above datasets, and the experimental results show that our proposed method achieves good recognition rates on each of the datasets. Importantly, we achieved a 93.47% recognition rate on the fused dataset, which was 2.31% and 26.08% higher than using ResNet and HOG alone. Full article

The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provide a significant tool in assisting medical intervention. Precise vein extraction would help medical workers to exactly determine the needle entry point to efficiently gain intravenous access for different clinical purposes, such as intravenous therapy, parenteral nutrition, blood analysis and so on. It would also eliminate repeated attempts at needle pricks and even facilitate an automatic injection procedure in the near future. In this paper, we present a combination of unsupervised and supervised dorsal hand vein segmentation from near-infrared images in the NCUT database. This method is convenient due to the lack of expert annotations of publicly available vein image databases. The novelty of our work is the automatic extraction of the veins in two phases. First, a geometrical approach identifies tubular structures corresponding to veins in the image. This step is considered gross segmentation and provides labels (Label I) for the second CNN-based segmentation phase. We visually observe that different CNNs obtain better segmentation on the test set. This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). The segmentation result of the ensemble is considered the second label (Label II). In our opinion, the new Label II is a better annotation of the NCUT database than the Label I obtained in the first step. The efficiency of computer vision algorithms based on artificial intelligence algorithms is determined by the quality and quantity of the labeled data used. Furthermore, we prove this statement by training ResNet–UNet in the same manner with the two different label sets. In our experiments, the Dice scores, sensitivity and specificity with ResNet–UNet trained on Label II are superior to the same classifier trained on Label I. The measured Dice scores of ResNet–UNet on the test set increase from 90.65% to 95.11%. It is worth mentioning that this article is one of very few in the domain of dorsal hand vein segmentation; moreover, it presents a general pipeline that may be applied for different medical image segmentation purposes. Full article

Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high. Full article

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins. Full article

In this study, near-infrared (NIR) technology was utilized to develop a low-cost real-time near infrared (NIR) guiding device for cannulation. A portable device that can be used by medical practitioners and also by students for their skills development training in performing cannulation. Methods. First, is the development of a reflectance type optical vein finder using three (3) light emitting diode (LED) lights with 960 nm wavelength, complementary metal-oxide-semiconductor-infrared (CMOS-IR) sensor camera with 1920 × 1080 UXGA (1080P), IR filter set for the given wavelength, and an open-source image processing software. Second, is the actual in-vitro human testing in two sites: the arm and dorsal hand of 242 subjects. The following parameters were included, such as gender, age, mass index (BMI), and skin tone. In order to maximize the assessment process towards the device, the researchers included the arm circumference. This augmented subcutaneous vein imaging study using the develop vein finder device compared the difference in the captured vein images through visual and digital imaging approaches. The human testing was performed in accordance with the ethical standards of the Trinity University of Asia—Institutional Ethics Review Committee (TUA—IERC). Results. The NIR imaging system of the developed vein finder in this study showed its capability as an efficient guiding device through real-time vein pattern recognition, for both sites. Improved captured vein images were observed, having 100% visibility of vein patterns on the dorsal hand site. Fourteen (5.79%) out of 242 subjects reported non-visible peripheral subcutaneous veins in the arm sites. Conclusions. The developed vein finder device with the NIR technology and reflected light principle with low-energy consumption was efficient for real-time peripheral subcutaneous vein imaging without the application of a tourniquet. This might be utilized as a guiding device in locating the vein for the purpose of cannulation, at a very low cost as compared to the commercially available vein finders. Moreover, it may be used as an instructional device for student training in performing cannulation. Full article

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality. Full article

This paper presents the development of a hybrid feature—dorsal hand vein and dorsal geometry—modality for human recognition. Our proposed hybrid feature extraction method exploits two types of features: dorsal hand geometric-related and local vein pattern. Using geometric affine invariants, the peg-free system extracts minutia points and vein termination and bifurcation and constructs a set of geometric invariants, which are then used to establish the correspondence between two sets of minutiae—one for the query vein image and the other for the reference vein image. When the correspondence is established, geometric transformation parameters are computed to align the query with the reference image. Once aligned, hybrid features are extracted for identification. In this study, the algorithm was tested on a database of 140 subjects, in which ten different dorsal hand geometric-related images were taken for each individual, and yielded the promising results. In this regard, we have achieved an equal error rate (EER) of 0.243%, indicating that our method is feasible and effective for dorsal vein recognition with high accuracy. This hierarchical scheme significantly improves the performance of personal verification and/or identification. Full article

Transillumination is a very well-known non-invasive optical technique that relies on the use of non-ionizing radiation to obtain information about the internal morphology of biological tissues. In a previous work, we implemented a laser-based illuminator operating at a wavelength of 850 nm, combined with a CMOS digital camera and narrow-band optical detection that showed great potential for in vivo imaging. A great advantage is the use of low-cost semiconductor lasers, driven by a very low current (about 11 mA, spatially distributed as a 6-by-6 matrix covering a 25 cm² area). Thanks to the strong absorption of hemoglobin at this wavelength, we have collected raw data of vascular structures that have been further processed to achieve images with much better quality. In particular, here we show that a higher contrast can be attained by the expansion of gray level histograms to exploit the full range, 0–255. This elaboration can be, for instance, exploited for the recognition of vascular structures with better resolution. Examples are reported relative to hand dorsal vein patterns and live chick embryos’ blood vessels. Analyses can be successfully performed without applying any thermal or mechanical stress to the human tissue under test and without damaging or puncturing any parts of the eggshell. Full article

Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics. Full article

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%. Full article

Transillumination with non-ionizing radiation followed by the observation of transmitted and diffused light is the simplest, and probably the oldest method to obtain qualitative information on the internal structure of tissues or body sections. Although scattering precludes formation of high-definition image (unless complex techniques are employed), low resolution pictures complemented by information on the functional condition of the living sample can be extracted. In this context, we have investigated a portable optoelectronic instrumental configuration for efficient transillumination and image detection, even in ambient day-light, of in vivo samples with thickness up to 5 cm, sufficient for visualizing macroscopic structures. Tissue illumination is obtained with an extended source consisting in a matrix of 36 near infrared Vertical Cavity Surface Emitting Lasers (VCSELs) that is powered by a custom designed low-voltage current driver. In addition to the successful acquisition of morphological images of the hand dorsal vein pattern, functional detection of physiological parameters (breath and hearth rate) is achieved non-invasively by means of a monochrome camera, with a Complementary Metal Oxide Semiconductor (CMOS) sensor, turned into a wavelength selective image detector using narrow-band optical filtering. Full article