Hand Vein Research Articles

Correlation and agreement between arterial and venous blood gas analysis in patients with hypotension—an emergency department-based cross-sectional study

BackgroundBlood gas analysis is integral to assessing emergency department (ED) patients with acute respiratory or metabolic disease. Arterial blood gas (ABG) is the gold standard for oxygenation, ventilation, and acid–base status but is painful to obtain. Peripheral venous blood gas (VBG) is a valuable alternative as it is less painful and easy to collect. The comparability of ABG and VBG was studied in various conditions. But in hypotension, previous findings were inconsistent. So, we studied the correlation and agreement between ABG and VBG in hypotensive patients.MethodologyThe study was conducted at the emergency department of a tertiary healthcare center in Northern India. Patients with hypotension above 18 years who satisfied the inclusion criteria were clinically evaluated. Patients who require ABG as a part of routine care were sampled. ABG was collected from the radial artery. VBG was obtained from the cubital or dorsal hand veins. Both samples were collected within 10 min and were analyzed. All ABG and VBG variables were entered in premade proforma. The patient was then treated and disposed of according to institutional protocol.ResultsA total of 250 patients were enrolled. The mean age was 53.25 ± 15.71 years. 56.8% were male. The study included 45.6% septic, 34.4% hypovolemic, 18% cardiogenic, and 2% obstructive shock patients. The study found a strong correlation and agreement for ABG and VBG pH, pCO2, HCO3, lactate, sodium, potassium, chloride, ionized calcium, blood urea nitrogen, base excess, and arterial/alveolar oxygen ratio. Hence, regression equations were made for the aforementioned. There was no correlation observed between ABG and VBG pO2 and SpO2. Our study concluded that VBG could be a reasonable alternative for ABG in hypotensive patients. We can also mathematically predict values of ABG from VBG using regression equations derived.ConclusionsABG sampling causes most unpleasant experiences to patients and is associated with complications like arterial injury, thrombosis, air or clotted-blood embolism, arterial occlusion, hematoma, aneurysm formation, and reflex sympathetic dystrophy. The study has shown strong correlations and agreements for most ABG and VBG parameters and can predict ABG mathematically using regression formulas formulated from VBG. This will decrease needle stick injury, consume less time, and make blood gas evaluation easy in hypotensive settings.

Dynamic Multihop Routing in Terahertz Flow-Guided Nanosensor Networks: A Reinforcement Learning Approach

The Internet of Nano-Things (IoNT) is an emerging paradigm in which devices sized to the nanoscale (nanonodes) and transmitting in the terahertz (THz) band can become decisive actors in future medical applications. Flow-guided nanonetworks are well-known THz networks aimed at deploying the IoNT inside the human body, among other issues. In these networks, nanonodes flowing through the bloodstream monitor-sensitive biological/physical parameters and dispatch these data via electromagnetic (EM) waves to a nanorouter implanted in human tissue, which operates as a gateway to external Internet connectivity devices. Under these premises, two shortcomings arise. First, the use of the THz band greatly limits the nanonode’s communication range. Second, the nanonodes lack resources for processing, memory, and batteries. To minimize the impact of these concerns in EM nanocommunications, a novel dynamic multihop routing scheme is proposed to model in-body, flow-guided nanonetwork architecture. To this end, a reinforcement learning-based framework is conceived, combining the features of EM nanocommunications and hemodynamics or fluid dynamics applied to the bloodstream. A generic Markov decision process (MDP) approach is derived to maximize the throughput metric, analytically modeling: 1) the movement of the nanonodes in the bloodstream as laminar flow; 2) energy consumption (including energy-harvesting issues); and 3) prioritized events. A thoroughly THz flow-guided nanonetwork case of study is also defined. Under the umbrella of this case, diverse testbeds are planned to create a procedure of evaluation, validation, and discussion. Results reveal that multihop scenarios obtain better performance than direct nanonode-nanorouter communication, specifically, the two-hop scenario, which, for instance, quadrupled the throughput in a hand vein without sharply penalizing other aspects such as energy consumption.

In vivo measurement of attenuation coefficient of blood in a dorsal hand vein in a frequency range of 10–45 MHz: A preliminary study

In this study, the attenuation coefficient of blood was measured in vivo in the frequency range of 10–45 MHz. A procedure to correct the distribution of sound pressure in the measurements was discussed. Further, in vivo measurements were applied on the dorsal hand vein of four healthy subjects at rest and during avascularization. As a preliminary result, less variation of the measured attenuation coefficients was achieved by the proposed method. The comparable results of the inclination of the attenuation coefficients were obtained at rest and during avascularization. Furthermore, the attenuation coefficients during avascularization were markedly higher than those at rest, reflecting the degree of red blood cell aggregation promoted by avascularization. This method may aid in the non-invasive evaluation of blood properties reflecting the degree of red blood cell aggregation.

Examining the association between malaria infection and the occurrence of ABO/Rhesus and hemoglobin variants in patients at a tertiary hospital in Port Harcourt, Nigeria

The spread of the ABO-Rh blood group and the haemoglobin genotype differs widely between racial and ethnic groupings. Patterns could fluctuate over time within the same community in various regions of the globe, which might indicate that a specific blood type gave resistance to an infectious illness. Therefore, it is crucial to have accurate and current information on how these blood types are distributed across human demographics. A total of 451 individuals were chosen at random, ranging in age from 1 to 15 years. The sample size was selected utilising Cochran sample size calculation as well as a rate of plasmodium in youngsters in Port Harcourt of 60.6%. Using a normal phlebotomy method, 2 ml of venous blood was extracted with little immobility within sterile circumstances from the dorsum of the hand or ante-cubital vein, as the occasion may indeed be. Employing thick and thin Giemsa-stained blood smears, malaria density was evaluated microscopically. The genotype of haemoglobin was determined using cellulose acetate membrane electrophoresis in Tris-EDTA borate buffer (PH 8.9). Atlas Medical ABO reagent was used to identify ABO blood type qualitatively. Data management and statistical analyses were carried out using the Statistical Analyses System SAS 9.4 (SAS Institute, Cary, North Carolina, USA), with p values less than.05 deemed statistically significant. Four hundred fifty-one (451) people took part in this study. The amount of research participants (394) tested rhesus positive (87.4%). Amongst ABO variations, blood type B+ 8 (32%), A+ 7 (28%), and O+ 6 (24%), were shown to be higher common with complex plasmodium. Blood type O+ 198 (46.48) was by far the most common of simple ABO/Rhesus + blood groups, followed by B+ 97(22.77%) and A+ 72 (22.77%). (16.90). Similarly, among ABO/Rhesus negative blood groups with uncomplicated falciparum malaria, blood group O- predominated with 41 (9.62%). A+, A-, B+, B-, AB+, O+, and O- blood types of class for uncomplicated plasmodium species load revealed a mean of 18114.03/l, 52760.10/l, 20021.47/l, 3428.33/l, 11084.33/l, 12460.34/l, and 4445.59/l in the identical sequence, with O- exhibiting the least susceptibility to malaria. In addition, for uncomplicated malaria parasite density, the haemoglobin genotype status indicated a mean of 113073.44/l, 111435.65/l, and 107250.3/1l for AA, AS, and SS in the same order, with AA indicating the highest sensitivity to plasmodium. The prevalence of haemoglobin genotypes AA, AS, and SS in the study participants was 72.5%, 20.6%, and 6.9%, respectively. Men made up 54.5% (246/451), although females made up 45.5% (205/451). The haemoglobin genotype AA predominated, with men (178) outnumbering girls (149), followed by AS (56 vs 37) and SS (12 against 31), correspondingly. The most frequent ABO blood type was O (54.3%), second by B (24.6%), A (19.7%), and AB (1.3%). The bulk of Rh+ blood cells (87.4%) were found in the Rh bloodstream. The bulk of Rh bloodstream (87.4%) were Rh+, whereas 12.6% comprised Rh. O+ (45.2%), B+ (23.3%), A+ (17.5%), O- (9.1%), A- (2.2%), AB+ (1.3%), B- (1.3%), and AB (0%) were always the mixed ABO and Rh blood types. Men’s blood groups B+ (60), B- (4), AB+ (4), and O+ (112) were significantly higher than female blood groups B+ (45), B- (2), AB+ (2), and O+ (94), whereas female blood groups A- (7) and O- (23) were higher than male blood types A- (2) and O- (23) (16).

Usability study of various biometric techniques in bank branches

The purpose of the presented research was to evaluate the performance of the prepared biometric algorithms and obtain information on the opinions and preferences of their users in bank branches. The study aimed to determine users' attitudes towards particular modalities and preferences on how to use biometrics after the bank customers had practical experience with the operation of the prototype solutions. The research results allow a better understanding of users' needs and expectations, which can help to increase the knowledge of the need for biometric solutions in banking. Making appropriate changes based on the research results can help improve user satisfaction. The experiments included the collection of biometric sample collection and verification processes conducted by bank customers assisted by employees. The study collected biometric samples and questionnaires from 365 people, and 179 complete verifications were conducted. About 95% of those surveyed expressed acceptance of the implemented solutions for handling transactions using biometrics. The HandVein-FaceImage (hand vein scanning and face recognition) modality pair was rated highest in 70% of cases in the studied population.

Psychodermatology: The Significant Interaction Between the Mind and Skin

Background: Fluid therapy in perioperative cesarean section remains a debated topic. Its purpose is to maintain or restore circulating blood volume and may affect the plasma acid-base balance of mother and neonatal. Therefore, the aim of this study was to compare the effects of Normal saline with Ringer's lactate serum on the acid-base status of mother and neonate in elective cesarean section. Methods: forty-eight healthy term parturient women scheduled for elective cesarean section were randomly divided into two groups (24 patients each). The first group received Normal saline serum and the second group received ringer lactate serum. Maternal blood samples were obtained from an indwelling cannula in a peripheral hand vein and Fetal arterial and venous blood samples were obtained at birth from a double-clamped umbilical cord. The data collected during the study included PH, PCO2, HCO3 and BE. At birth, 1- and 5-minute Apgar scores were recorded. Data were analyzed using descriptive statistics and inferential statistics at a significance level of less than 0.05. Results: Patients did not differ significantly in terms of demographic characteristics. The acidosis in mothers was higher in lactated ringer group (5%, p <0.001). There was no difference in Apgar score between the two groups. Conclusion: Normal saline compared to lactated ringer causes acidosis in mothers under elective cesarean section with spinal anesthesia, but none of them has a significant effect on the acid-base status of the infant. Appropriate fluid therapy aimed at optimization of pH can be effective for mothers who are at higher risk for acidosis.

Attack Detection for Finger and Palm Vein Biometrics by Fusion of Multiple Recognition Algorithms

Vascular patterns in the hand region are not visible to the naked eye or consumer cameras, therefore finger and hand vein biometrics is often considered invulnerable to presentation attacks. However, one can never rule out the possibility that a malicious attacker manages to create functional attack samples. Various approaches on how to detect such attacks have been proposed, along with publicly available attack databases and varying ideas to create artificial attack samples. In a first step it is important to verify that created presentation attack artifacts hold the potential to deceive a real system. In order to provide a meaningful and comparable threat potential evaluation, this article evaluates 15 existing vein recognition schemes using attack samples derived from three finger vein attack databases and one palm vein attack database. As a second step, in this work we investigate an approach to combine these employed vein recognition schemes and utilizing them to perform presentation attack detection, which to the authors’ best knowledge has not been described in literature so far. Experimental results show that this approach can effectively be used to detect vein attack samples.

Recognition of Dorsal Hand Vein in Small-Scale Sample Database Based on Fusion of ResNet and HOG Feature

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.

Dorsal hand Vein Authentication System Using Convolution Neural Network

This study proposes using a dorsal hand vein authentication system using transfer learning from convolutional neural network models of VGG16 and VGG19. The required images were obtained from Bosphorus Hand Vein Database. Among the 100 users, the first 80 users were treated as registered users, while the remaining users as unregistered users. 960 left-hand images of the registered users were trained during the training phase. Meanwhile, 100 images, consisting of 80 registered and 20 unregistered users, were randomly selected for the authentication application testing. Our results showed that VGG19 produced a superior validation accuracy as compared to that of VGG16 given by 96.9% and 94.3%, respectively. The testing accuracy of VGG16 and VGG19 is 99% and 100%, respectively. Since VGG19 is shown to outperform its shallower counterpart, we implemented a User Interface (UI) based on VGG19 model for dorsal hand vein identification. These findings indicate that our system may be deployed for biometric authentication in the future for a more efficient and secure implementation of person identification and imposter detection

Dorsal hand veins features extraction and recognition by correlation coefficient

One of the most convenient biometrics approaches for identifying a person is dorsal hand veins recognition. In recent years, the dorsal hand veins have acquired increasing attention because of its characteristics such as universal, unique, permanent, contactless, and difficulty of forging, also, the veins remain unchanged when a human being grows. The captured dorsal hand veins image suffers from the many differences in lighting conditions, brightness, existing hair, and amount of noise. To solve these problems, this paper aims to extract and recognize dorsal hand veins based on the largest correlation coefficient. The proposed system consists of three stages: 1) preprocessing the image, 2) feature extraction, and 3) matching. In order to evaluate the proposed system performance, two databases have been employed. The test results illustrate the correct recognition rate (CRR), and accuracy of the first database are 99.38% and 99.46%, respectively, whereas the CRR, and accuracy of the second database are 99.11% and 99.07% respectively. As a result, we conclude that our proposed method for recognizing dorsal hand veins is feasible and effective.

Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants

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.

A novel security and authentication method for infrared medical image with discrete time chaotic systems

Objective:Hand vein images have become important biometric signs used for identification systems. Also, dorsal hand vein images have noteworthy advantages in terms of reliability and contactless procedure. Surgically changing the vascular pattern under the skin is extremely difficult. Therefore, the use of such patterns in identity recognition applications is increasing day by day. In this context, many studies have been carried out in essential areas such as image acquisition for different textures, image preprocessing, data security, image feature extraction and recognition. Methods:In this article, a new dorsal hand vein identification application has been carried out with the chaos-based security mechanism to protect personal data and the Improved SURF method to reduce the error matches of traditional SURF method in the current application. There are two improvements in the presented system. The first one finds the features representing only the vein segments, not the remaining tissue or hairly parts. The second one eliminates the mismatching points to increase the accuracy. Results:Both encryption and data hiding techniques are preferred together to protect person data. In addition, dorsal hand vein images taken with a camera for matching and recorded in the database of the people have been improved with the newly developed I-SURF feature extraction method. Thereby, with the developed application, in addition to keeping the personal identity information securely in the database with chaos-based methods, higher accuracy rate has been achieved in matching the new image taken with the camera.

Dorsal hand vein recognition based on transmission-type near infrared imaging and deep residual network with attention mechanism

Dorsal hand vein recognition based on transmission-type near infrared imaging and deep residual network with attention mechanism

Dorsal Hand Vein Identification using Transfer Learning from AlexNet

Dorsal hand vein pattern is a highly secured biometric system that has been used in many applications due to its non-contact attributes. Prior studies focused on investigation of different deep networks for hand vein classification task using different training parameters. It is the aim of this study to propose the use of systematic fine-tuning system for identifying the best parameters value for enhanced model learning efficiency. In this study, pre-trained AlexNet was trained using Bosphorus hand vein database for identification of 100 users. The experiments were carried out using original images, and preprocessed (cropped) images for comparison. The testing accuracies of these datasets were compared following tuning of training parameters, namely trainingand testing split ratio, number of epochs, mini-batch size and initial learning rate. It was observed that the testing accuracy of the model trained using cropped images is higher than that using the original images. The model from preprocessed dataset shows a good testing accuracy of 96 % using a split ratio of 90:10, epoch 50, mini-batch-size of 10 and an initial learning rate of 0.0001. The performance of our trained model is more superior than many reported results in the field. In future, the performance of this classification system may be further enhanced with automatic search of parameters for improved model training efficiency.

Techniques to Improve Peripheral Intravenous Catheter Placement

Techniques to Improve Peripheral Intravenous Catheter Placement

V-Vibe: A robust ROI extraction method based on background subtraction for vein images collected by infrared device

In recent years, the global epidemic has put forward higher requirements for public health security, and the non-contact finger vein acquisition and identification technology based on infrared imaging has become a research hotspot due to its high security, accuracy and convenience. However, infrared images generally have the problems of low resolution, low contrast and low signal-to-noise ratio, which seriously affect the effective extraction and utilization of image feature information. Moreover, most of the images captured by unconstrained vein acquisition devices have a large number of complex backgrounds, which are often similar to the pixel distribution of finger region, which greatly affects the recognition performance of vein images. To solve the above problems, we carried out the following exploratory work. First, we designed and developed a new finger vein infrared acquisition device. The device adopts a kind of physical structure of triangular spotlight, and in combination with our proposed V-Vibe method can achieve complete Region of Interest (ROI) extraction of finger vein images. Second, we created a new finger vein dataset (named FV-BS) for background subtraction. The dataset contains 420 individuals, 4,200 finger vein image samples and 420 device background images, as well as a subset of vein image data filled with complex backgrounds. Third, we proposed a new method named V-Vibe to extract ROI from vein images by using background subtraction technology. Subtraction can effectively eliminate factors that affect recognition performance in both vein images and background sample set, thus greatly reducing the influence of complex backgrounds and low-quality infrared images on identification. To verify the validity and accuracy of the V-Vibe method, we designed and conducted a large number of experiments on several datasets, including four publicly available datasets, as well as self-built FV-BS, and analyzed the results in detail. Compared with other methods, the ROI extraction effect of V-Vibe method proposed in this paper is significantly improved, and EER is reduced by 1.18% and 2.37% on average respectively in maximum curvature and repeated line tracking feature extraction methods, which is an effective method for ROI extraction of finger vein images. Furthermore, we also tested the proposed V-Vibe method to extract ROI from the vein images of palm and back of hands. Experimental results show that our method has excellent segmentation accuracy on both palm and dorsal hand vein datasets, with the average F1 Score reaching 95.1%.

An improved YOLO Nano model for dorsal hand vein detection system.

To date, venipuncture, the most necessary and fundamental medical means, still remains a challenging task for medical stuff due to significant individual differences in vein condition. Thanks to mature development in near-infrared (NIR) imaging technology, a series of venepuncture auxiliary equipment has been devised and put into use. Yet, previous researches concentrated more on vein pattern segmentation, failing to materialize the identification of veins suitable to puncture in an embedded system. Given the above, we propose an approach to detect and locate the optimal veins fully utilizing the state-of-the-art deep learning and image processing technologies in order to provide a more practical reference. Firstly, a dedicated NIR-based puncturable vein positioning system is designed, realizing collection of dorsal hand vein images as well as the rapid and accurate location of veins suitable to puncture. Secondly, considering the limitations of embedded devices on computation ability and memory, an improved network based on YOLO Nano, named YOLO Nano-Vein, is presented with architecture trimmed, output scales reduced, and an atrous spatial pyramid pooling (ASPP) added. Finally, average precision (AP) is increased from 91.68 to 93.23%, and the detection time and parameters of network are reduced by 22% and 17.5%, respectively, which validates the proposed network achieves higher accuracy with less detection time in comparison with YOLO Nano and YOLOv3, indicating stronger applicability for detection tasks on embedded devices.

Effect of 6% hydroxyethyl starch pre-administration for reduction of pain on propofol injection: A placebo-controlled randomised study.

Background and Aims:Colloids modify the vascular endothelium and prevent contact activation of various substances. Pre-administration of colloids may prevent contact activation of vascular endothelium by propofol. The objective of this study was to evaluate the effect of 6% hydroxyethyl starch (HES) 130/0.4 pre-administration on propofol injection pain.Methods:Adult patients of the American Society of Anesthesiologists physical status I and II patients, 18-65 years old, of either gender and undergoing elective surgery were randomised into two groups. 100 mL bolus of HES or 0.9% normal saline (NS) was administered over three to five minutes through an 18 G cannula placed in the hand or forearm vein, followed by induction with 1% propofol premixed with 2% lidocaine. Pain during propofol injection was assessed every 10 seconds before the loss of verbal contact as 0- no pain; 1- mild pain evident only on questioning after 10 seconds without any obvious discomfort; 2-moderate pain self-reported by patients within 10 seconds with some discomfort; and 3- severe pain accompanied by withdrawing of hand, and behavioural signs.Results:126 patients completed the study. Overall incidence of pain was significantly higher in the NS group vs HES group (53% vs 28%; P = 0.004; relative risk 1.54, 95% confidence interval 1.13-2.09). Incidence of severe (8% vs 0%) and moderate pain (16% vs 5%) was higher in the NS group, while the incidence of mild pain was comparable (29% vs 23%; NS vs HES). A significant difference was seen in the severity of pain between the groups (P = 0.002).Conclusion:Pre-administration of 100 mL bolus of 6% HES 130/0.4 significantly reduced propofol injection pain.

Staged or simultaneous varicose vein treatment after saphenous vein ablation

Chronic venous disease (CVD) is a progressive condition that affects a significant percentage of the population. Clinical manifestation of CVD differs between the patients: from asymptomatic patients with esthetic problems only to the very severe stages (including venous leg ulcer) that significantly decrease the quality of life (Qol). Although the varicose vein patients can be asymptomatic and they decide for treatment only for cosmetic reasons, many of them present the symptoms and/or signs of CVD, including pain, heaviness, itching, cramps, swelling, trophic changes or ulcerations. Therapeutic management of varicose veins includes surgery, minimally invasive procedures (involving saphenous ablation or sclerotherapy), compression therapy and pharmacological treatment. Traditional surgical treatment has been a leading method in invasive VV management for many years. Nonetheless, minimally invasive treatment thrived in the 21st century and overtook open surgery regarding VVS and more advanced stages of CVD. Another commonly used minimally invasive method in the treatment of VV is foam sclerotherapy. The efficacy of EVLA in the treatment of VV can be improved by performing adjunctive foam sclerotherapy (FS) of the tributaries. The combination of EVLA and FS is an effective method of reducing the rate of reinterventions in VV patients with saphenous vein incompetence. In the patients undergoing saphenous ablation, VV treatment (FS or mini phlebectomy) can be performed within the same procedure or as the delayed treatment. The argument for delayed treatment is the potential possibility of the VV regression (partial or complete) after saphenous vein ablation. On the other hand, saphenous ablation and varicose vein treatment within the same session result in fast and complete varicose vein removal without the need for additional procedures. Nevertheless, there is no consensus regarding the optimal timing of performing FS after EVLA of the GSV trunk and because of many diversified scientific reports there are still different approaches to this problem in phlebological centres around the world. Since the timing of adjunctive FS after EVLA procedure in the treatment of VV associated with GSV incompetence is a topic of open debate among surgeons, this study is a review that compares concomitant and staged treatments of VV.

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.