Vein Images Research Articles

Superficial venous morphometry in the antecubital fossa: An autonomous robotic ultrasound-based analysis.

The antecubital fossa is an important site for venepuncture and intravenous procedures. The size and location of a vein can affect the success of venepuncture and intravenous access. Several studies have investigated the superficial vein morphometry, but they had small sample sizes or focused on specific populations or groups. Therefore, we conducted a prospective study with large participants in general population to analyse the morphology of the antecubital superficial vein and identify the association of sex, age and body mass index (BMI) with the size and location of the vein. This study collected images of superficial veins prospectively using autonomous robotic ultrasound on the antecubital area between October and November 2020. We measured the superficial vein depth, vertical diameter and horizontal diameter at the antecubital area, extracted population characteristics (sex, age and BMI), and analysed a relationship between the vein dimensions and the characteristics. In this study, data from 461 participants (201 males and 260 females) with mean age of 41.1 years were produced. The mean vein depth, mean vertical diameter and mean horizontal diameter (±standard deviation) were 4.81 (±2.17), 3.01 (±1.10) and 4.46 (±1.60) mm, respectively. We found significant differences in vein dimensions between males and females, with males having larger vertical and horizontal diameters than females (p < 0.001). The study also revealed significant differences in vein depth and dimensions among age groups and BMI subgroups (p < 0.001). These findings revealed that the superficial vein in the antecubital area was oval, with a larger horizontal diameter than vertical diameter. Morphometry revealed differences in sex, age and BMI. Understanding variations in vein dimensions among different subgroups can help medical professionals improve success rate of venous access and patient safety.

Sequence-wise multimodal biometric fingerprint and finger-vein recognition network (STMFPFV-Net)

The existing multimodal biometric fingerprint and vein deep learning features were found effective for biometric recognition. However, the current performance of the deep learning features is limited due to missing temporal image dependence and the extracted features can obfuscate some vital information because of irrelevant image features. This work proposes a sequence of filtered spatial and temporal multimodal fingerprint and finger vein network (FS-STMFPFV-Net). The overall proposed FS-STMFPFV-Net is achieved from two-channel independent learning to improve image variabilities. In the first channel, the image sequence is generated by aligning the fingerprint and finger vein images together inside the image generator. The sequences are built into the five layers of a deep convolution neural network fusion model to extract spatial sequence-wise features. The second channel is where the extracted sequence-wise features are remembered in the long short-term memory for interactions between temporal and sequence dimensions which finally generate their long-term temporal dependencies as complementary information. These sequences are fused together, and the discriminative sequence features are selected using feature selection. We have presented the ReliefFS feature selection which serve as a complementary basis for selecting compact features from the extracted CNN-based features. To evaluate the performance of the proposed FS-STMFPFV-Net, the NUPT-FPV, FVC-2002-DBs, and the CASIA dataset were used, which provides fingerprint, finger-vein, and palmprint image databases, for experimental validation. The proposed FS-STMFPFV-Net when evaluated using standard protocols offers more than 97% accuracy across the different databases and is ten times more computationally friendly than the existing algorithms.

An improved intelligent water drops feature selection for finger vein recognition

In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively.

Parallel interrogation of the chalcogenide-based micro-ring sensor array for photoacoustic tomography

Photoacoustic tomography (PAT), also known as optoacoustic tomography, is an attractive imaging modality that provides optical contrast with acoustic resolutions. Recent progress in the applications of PAT largely relies on the development and employment of ultrasound sensor arrays with many elements. Although on-chip optical ultrasound sensors have been demonstrated with high sensitivity, large bandwidth, and small size, PAT with on-chip optical ultrasound sensor arrays is rarely reported. In this work, we demonstrate PAT with a chalcogenide-based micro-ring sensor array containing 15 elements, while each element supports a bandwidth of 175 MHz (−6 dB) and a noise-equivalent pressure of 2.2 mPaHz−1/2. Moreover, by synthesizing a digital optical frequency comb (DOFC), we further develop an effective means of parallel interrogation to this sensor array. As a proof of concept, parallel interrogation with only one light source and one photoreceiver is demonstrated for PAT with this sensor array, providing images of fast-moving objects, leaf veins, and live zebrafish. The superior performance of the chalcogenide-based micro-ring sensor array and the effectiveness of the DOFC-enabled parallel interrogation offer great prospects for advancing applications in PAT.

Local Attention Transformer-Based Full-View Finger-Vein Identification

Multi-view finger-vein recognition technology has attracted increasing attentions in recent years. Despite recent advances in the multi-view finger-vein identification, existing solutions employ multiple monocular cameras from different views to record two-dimensional (2D) projections of 3D vein vessels, which causes the following problems: 1) 2D images collected from limited views (two or three views) are insufficient for robust 3D vein vessel feature representation. Furthermore, image sequences of the same finger acquired from different views usually show significant differences. As a result, the existing works are still sensitive to positional variations of the fingers, specifically those caused by finger roll movements. 2) Using multiple cameras can lead to increased costs. Moreover, it is impossible to employ several cameras to acquire full-view images because of the limited space on capturing devices. To address the above issues, we present FV-LT, a Full-View Finger-Vein identification system based on a Local attention Transformer, by implementing an image acquisition device with a single camera. First, we design and implement a finger-vein acquisition prototype device that utilizes a single camera and a LED group to rotate along a finger for full-view image collection. This allows capturing all vein patterns concealed beneath human skin to form a complete representation of finger features. Second, given the full-view vein images, we propose a local attention transformer-based approach to extract dependency features of a token (a patch or an image) on its neighborhood’s tokens among image patches and among full-view images, respectively. These dependency features are shown to be robust to positional variations induced by finger rolls. Based on the public database of full-view finger-vein images captured by our designed device, we verify the performance of the proposed FV-LT. The experimental results show that FV-LT significantly outperforms existing 2D/multi-view based approaches with respect to improving the tolerance against finger roll and achieving the state-of-the-art identification accuracy.

The Impact of Neighborhood Social Disadvantage on Presentation and Management of Hemodialysis Access Surgery Patients

The impact of social determinants of health on the presentation, management, and outcomes of patients requiring hemodialysis (HD) access surgery have not been well characterized. The Area Deprivation Index (ADI) is a validated measure of aggregate community-level social determinants of health disparities experienced by members within a community. Herein, we examined the effect of ADI on health outcomes for first-time HD access patients. We identified patients who underwent first-time HD access surgery in the Vascular Quality Initiative between August 2011 and May 2022. Patient zip codes were correlated with an ADI quintile (Q1-Q5, least to most disadvantaged). Patients without ADI were excluded. Perioperative and long-term outcomes considering ADI were analyzed. There were 43,292 patients analyzed. The average age was 63 years, 43% were female, 60% were of White race, 10% were of Hispanic ethnicity, and 85% received autogenous arteriovenous (AV) access. Patient distribution by ADI quintile (Q) was as follows: Q1 (16%), Q2 (18%), Q3 (21%), Q4 (23%), and Q5 (22%). On multivariable analysis, the most disadvantaged quintile (Q5) was associated with decreased autogenous access creations (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.77-0.94; P = .002), fewer preoperative vein imaging (OR, 0.62; 95% CI, 0.50-0.77; P < .001), lower access maturation (OR, 0.82; 95% CI, 0.71-0.94; P = .005), and lower 1-year survival (OR, 0.80; 95% CI, 0.71-0.90; P < .001) compared to Q1. Q5 was not associated with significantly higher 1-year intervention rates (Table). The most socially disadvantaged (Q5) AV access creation patients were more likely to experience disparities at the time of preoperative AV access creation, access type, as well as well as at 1-year outcomes. Improvement in preoperative planning and long-term follow-up may be an opportunity for advancing health equity in this population.TableUnivariate and multivariable analysis of outcomes of interest stratified by Area Deprivation Index (ADI) quintileADIAV fistulaVein imaging obtainedAccess matured at last follow-up1-Year intervention1-Year survivalUniMultiUniMultiUniMultiUniMultiUniMultiQ16138 (87)(ref)2413 (91)(ref)2695 (86)(ref)798 (31)(ref)6483 (92)(ref)Q26771 (85)0.84 [0.75-0.93]2254 (91)1.01 [0.80-1.26]2840 (83)0.83 [0.72-0.97]961 (32)0.98 [0.86-1.11]7121 (89)0.82 [0.72-0.93]Q37717 (85)0.81 [0.73-0.89]2575 (94)1.50 [1.18-1.90]3253 (83)0.82 [0.71-0.95]1089 (31)0.93 [0.82-1.05]8122 (89)0.80 [0.71-0.90]Q48211 (84)0.76 [0.69-0.84]2619 (91)0.96 [0.78-1.19]3529 (84)0.91 [0.78-1.05]1268 (32)0.94 [0.83-1.06]8844 (90)0.88 [0.79-0.99]Q57838 (84)0.85- [0.77-0.94]2018 (88)0.62 [0.50-0.77]3694 (82)0.82 [0.71-0.94]1433 (34)0.99 [0.88-1.12]8370 (90)0.80 [0.71-0.90]P value<.001-<.001-<.001-.047-<.001-Multi, multivariable analysis; Uni, univariate analysis.Univariate results represented as number (%). Multivariable results represented as adjusted odds ratio. Brackets in multivariable analysis indicated the 95% confidence interval. Bold indicated statistically significant results. Of patients with long-term follow-up data, the average length of last long-term follow-up data was 383 ± 240 days. Open table in a new tab

Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning.

Vessel invasion (VI) is an important factor affecting the prognosis of gastric cancer (GC), and the accurate determination of preoperative VI for locally advanced GC is of great clinical significance. Traditional methods for the evaluation of VI require postoperative pathological examination. Noninvasive preoperative evaluation of VI is therefore crucial to determine the best treatment strategy. To determine the value of preoperative prediction of gastric VI based on portal venous phase computed tomography (CT) radiomic features and machine-learning models, a retrospective analysis of 296 patients with locally advanced GC confirmed through pathological examination was performed. They were divided into two groups, VI+ (n=213) and VI- (n=83), based on pathological results. Using pyradiomics to extract two-dimensional radiomic features of the portal venous stage of locally advanced GC, data were divided into training (n=207) and validation sets (n=89), with a ratio of 7:3, and three feature selection methods were cascaded and merged. Finally, least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to obtain the optimal feature subset. Four current representative machine-learning algorithms were used to construct the prediction model, the receiver operating characteristic curve was constructed to evaluate the predictive performance of the model, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. The differentiation degree, and the Lauren's and CA199 classifications were independent risk factors for locally advanced GC VI. Pyradiomics extracted 864 quantitative features of portal vein images of locally advanced GC. After filtering out low variance features using R, 236 features remained. Next, 18 features were screened using the LASSO algorithm. Extreme gradient boosting (XGBoost), logistic regression, Gaussian naive Bayes, and support vector machine models were constructed based on the 18 best features screened out of the portal venous CT images of advanced GC and three independent risk factors of GC VI in clinical features predicted the training set AUC values of 0.914, 0.897, 0.880, and 0.814, respectively. The predicted validation set AUC values were 0.870, 0.877, 0.859, and 0.773, respectively. The DeLong test results indicated no statistically significant difference in AUC values between the XGBoost and logistic regression models in the training and validation sets. The four machine-learning models showed high predictive performance. The logistic regression model had the highest AUC value in the validation set (0.877), and the accuracy and F1 score were 77 and 87.6%, respectively. CT radiomic features and machine-learning models based on the portal venous phase can be used as a noninvasive imaging method for the preoperative prediction of VI in locally advanced GC. The logistic regression model exhibited the highest diagnostic performance.

Preoperative visualization of midline-crossing subcutaneous arteries in transverse abdominal flaps using photoacoustic tomography

Photoacoustic tomography is a noninvasive vascular imaging modality that uses near-infrared pulsed laser light and ultrasound to visualize vessels. We previously demonstrated the utility of photoacoustic tomography for anterolateral thigh flap surgery involving body-attachable vascular mapping sheets. However, it was not possible to obtain clear separate images of arteries and veins. In this study, we tried to visualize subcutaneous arteries that cross the midline of the abdomen, since these arteries are known to be important for obtaining large perfusion areas in transverse abdominal flaps. Four patients scheduled to undergo breast reconstruction with abdominal flaps were examined. Photoacoustic tomography was performed preoperatively. The tentative arteries and veins were traced according to the S-factor, an approximate hemoglobin oxygen saturation parameter calculated using 2 laser excitation wavelengths (756 and 797nm). Intraoperatively, arterial-phase indocyanine green (ICG) angiography was performed after abdominal flap elevation. Images of vessels speculated to be arteries by preoperative photoacoustic tomography were merged with those of intraoperative ICG angiography and analyzed in an 8×4-cm2 area below the umbilical region. The S-factor was used to visualize the midline-crossing subcutaneous arteries in all 4 patients. A matching analysis compared preoperative tentative arteries according to photoacoustic tomography with ICG angiography results in the 8×4-cm2 area below the umbilical region and indicated a 71.3-82.1% match (average: 76.9% match). This study demonstrates that the S-factor, a noninvasive, label-free imaging modality, can be used to successfully visualize subcutaneous arteries. This information can aid in selecting perforators for abdominal flap surgery.

A finger vein authentication system based on pyramid histograms and binary pattern of phase congruency

Finger vein recognition is a novel biometric method which is getting more attention recently because of its high security and convenience. However, there are still many interference while recognizing, such as uneven illumination caused by non-uniform near-infrared light source or image dislocation caused by finger position change. Therefore, this paper proposes a finger vein feature extraction strategy integrating binary pattern of phase congruency (BPPC) and pyramid of histograms of orientation gradients (PHOG). We first calculate the energy information of each part in different regions and represents it with binary pattern, and then analyze the shape and texture of finger vein with image pyramid of histogram, and finally fuses them to obtain better recognition efficiency. Our method considers the multi-scale features of finger vein images so that some localdisturbance like overexposure of light source or insufficient illumination can be ignored, and our method is suitable for most environments and finger vein collection equipment. We experimented on both public database and database built by our lab with recognition rate more than 98%, which confirmed that our method is effective and has good practical significance.

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net.

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.

A novel cancelable finger vein templates based on LDM and RetinexGan

In this paper, we propose a new biometric template protection scheme, which can deal with the finger vein biometric security threats, through using the LDM and RetinexGAN model. The RetinexGAN model is mainly used to handle the illumination and low contrast problems effectively, while efficiently extracting discriminative features from the finger vein images. The projection of extracted features into dissimilarity space is done using Local Dissimilarity Map (LDM). LDM is an efficient way for finger vein features representation, which investigates the relationships and correlation inter and intra classes, while effectively coming up with the accidental shifts/rotations caused by the arbitrary position of the finger during image acquisition. The proposed approach is successfully evaluated in terms of non-invertibility, non-linkability, revocability and performances. Experimental results and comparison analysis with the state of arts methods confirm that the proposed framework can achieve promising results.

Robotic Devices for Assisted and Autonomous Intravenous Access

Intravenous Access (IVA) is the most common invasive medical procedure. Globally, it is estimated that over one billion IVA devices (needles/catheters) are used annually. However, the overall failure rate in this procedure is unacceptably high, reaching values between 35 and 50%. This has driven a great deal of research and technological development over recent decades, including the integration of different levels of autonomy in IVA medical devices to greatly improve this process. This paper will review these recent technical developments, including methods and systems for vein imaging and localization, needle insertion, venipuncture detection, catheter placement, and complete robotic IVA platforms. Furthermore, this paper explores emerging technical aspects, current limitations, and new research directions that may enable wider clinical translation and better acceptance of robotic IVA technologies.

EIFNet: An Explicit and Implicit Feature Fusion Network for Finger Vein Verification

Finger vein recognition has received more attention in recent years due to its high security and promising development potential. However, extracting complete vein patterns and obtaining features from the original images suffer from the low contrast of finger vein images, which dramatically restrains the performance of finger vein recognition algorithms. Inspired by this motivation, we propose an explicit and implicit feature fusion Network (EIFNet) for finger vein verification. It can extract more comprehensive and discriminative features by complementarily fusing the features extracted from binary vein masks and gray original images. We design a feature fusion module (FFM) acting as a bridge between mask feature extraction module (MFEM) and contextual feature extraction module (CFEM) to achieve the optimal fusion of features. To obtain more accurate vein masks, we develop a novel finger vein pattern extraction method and provide the first finger vein segmentation dataset THUFVS.We solve the difficulty of building finger vein segmentation datasets in a simple but effective way, and develop a complete process encompassing dataset creation, data augmentation refinement and network design, which refers to the Mask Generation Module (MGM), for the deep learning based finger vein pattern extraction method. Experimental results demonstrate the superior verification performance of EIFNet on three widely used datasets compared with other existing methods.

Advanced Fourier migration for Plane-Wave vector flow imaging

Ultrafast ultrasound imaging modalities have been studied extensively in the ultrasound community. It breaks the compromise between the frame rate and the region of interest by imaging the whole medium with wide unfocused waves. Continuously available data allow monitoring fast transient dynamics at hundreds to thousands of frames per second. This feature enables a more accurate and robust velocity estimation in vector flow imaging (VFI). On the other hand, the huge amount of data and real-time processing demands are still challenging in VFI. A solution is to provide a more efficient beamforming approach with smaller computation complexity than the conventional time-domain beamformer like delay-and-sum (DAS). Fourier-domain beamformers are shown to be more computationally efficient and can provide equally good image quality as DAS. However, previous studies generally focus on B-mode imaging. In this study, we propose a new framework for VFI which is based on two advanced Fourier migration methods, namely, slant stack migration (SSM) and ultrasound Fourier slice beamform (UFSB). By carefully modifying the beamforming parameters, we successfully apply the cross-beam technique within the Fourier beamformers. The proposed Fourier-based VFI is validated in simulation studies, in vitro, and in vivo experiments. The velocity estimation is evaluated via bias and standard deviation and the results are compared with conventional time-domain VFI using the DAS beamformer. In the simulation, the bias is 6.4%, −6.2%, and 5.7%, and the standard deviation is 4.3%, 2.4%, and 3.9% for DAS, UFSB, and SSM, respectively. In vitro studies reveal a bias of 4.5%, −5.3%, and 4.3% and a standard deviation of 3.5%, 1.3%, and 1.6% from DAS, UFSB, and SSM, respectively. The in vivo imaging of the basilic vein and femoral bifurcation also generate similar results using all three methods. With the proposed Fourier beamformers, the computation time can be shortened by up to 9 times and 14 times using UFSB and SSM.

Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

As an emerging biometric technology, finger vein recognition has attracted much attention in recent years. However, single-sample recognition is a practical and longstanding challenge in this field, referring to only one finger vein image per class in the training set. In single-sample finger vein recognition, the illumination variations under low contrast and the lack of information of intra-class variations severely affect the recognition performance. Despite of its high robustness against noise and illumination variations, sparse representation has rarely been explored for single-sample finger vein recognition. Therefore, in this paper, we focus on developing a new approach called Progressive Sparse Representation Classification (PSRC) to address the challenging issue of single-sample finger vein recognition. Firstly, as residual may become too large under the scenario of single-sample finger vein recognition, we propose a progressive strategy for representation refinement of SRC. Secondly, to adaptively optimize progressions, a progressive index called Max Energy Residual Index (MERI) is defined as the guidance. Furthermore, we extend PSRC to bimodal biometrics and propose a Competitive PSRC (C-PSRC) fusion approach. The C-PSRC creates more discriminative fused sample and fusion dictionary by comparing residual errors of different modalities. By comparing with several state-of-the-art methods on three finger vein benchmarks, the superiority of the proposed PSRC and C-PSRC is clearly demonstrated.

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches.

Residual Gabor convolutional network and FV-Mix exponential level data augmentation strategy for finger vein recognition

Using deep learning to improve the performance of finger vein recognition has become a part of mainstream research. Currently, the performance of finger vein recognition systems is limited by insufficient finger vein training samples, which leads to insufficient feature learning and weak model generalization. To solve the above problems, first, we propose a simple and effective finger vein data augmentation strategy named FV-Mix, which uses fine finger vein image region of interest (ROI) for grayscale normalization and linear mixing. It can accomplish exponential-level data augmentation on the training sample, and the augmented data will represent a more complete dataset. Second, a residual Gabor convolutional network (RGCN) is designed for finger vein recognition, which includes a residual Gabor convolutional layer (RGCL) and a dense semantic analysis module (DSAM). The RGCL is designed to replace the shallow convolutional layer in the network by using the characteristics of the Gabor filter to enhance the scale and direction of information in the shallow pattern features. And then, the enhanced deep features are further extracted and analyzed by DSAM, which is used to assist the final model in classifying and recognizing finger vein images. To verify the effectiveness of our work, five publicly available finger vein image datasets are used, and a large number of comparison experiments are designed. The proposed FV-Mix strategy, RGCL module, and DSAM module were fully validated. The experimental results show that on these five datasets, the average recognition accuracy and equal error rate (EER) of the proposed RGCN are 99.22% and 0.188%, respectively, achieves competitive performance compared with current state-of-the-art work.

Deep Learning-Based Wrist Vascular Biometric Recognition

The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system.

The value of contrast-enhanced portal vein imaging at the hepatobiliary phase obtained with gadobenate dimeglumine for predicting decompensation and transplant-free survival in chronic liver disease.

To investigate the value of contrast-enhanced portal vein imaging at the hepatobiliary phase obtained with gadobenate dimeglumine for predicting clinical outcomes in patients with chronic liver disease (CLD). Three hundred and fourteen CLD patients who underwent gadobenate dimeglumine-enhanced hepatic magnetic resonance imaging were stratified into three groups: nonadvanced CLD (n = 116), compensated advanced CLD (n = 120), and decompensated advanced CLD (n = 78) groups. The liver-to-portal vein contrast ratio (LPC) and liver-spleen contrast ratio (LSC) at the hepatobiliary phase were measured. The value of LPC for predicting hepatic decompensation and transplant-free survival was assessed using Cox regression analysis and Kaplan-Meier analysis. The diagnostic performance of LPC was significantly better than LSC in evaluating the severity of CLD. During a median follow-up period of 53.0months, the LPC was a significant predictor for hepatic decompensation (p < 0.001) in patients with compensated advanced CLD. The predictive performance of LPC was higher than that of the model for end-stage liver disease score (p = 0.006). With the optimal cut-off value, patients with LPC ≤ 0.98 had a higher cumulative incidence of hepatic decompensation than patients with LPC > 0.98 (p < 0.001). The LPC was also a significant predictive factor for transplant-free survival in patients with compensated advanced CLD (p = 0.007) and those with decompensated advanced CLD (p = 0.002). Contrast-enhanced portal vein imaging at the hepatobiliary phase obtained with gadobenate dimeglumine is a valuable imaging biomarker for predicting hepatic decompensation and transplant-free survival in CLD patients. • The liver-to-portal vein contrast ratio (LPC) significantly outperformed liver-spleen contrast ratio in evaluating the severity of chronic liver disease. • The LPC was a significant predictor for hepatic decompensation in patients with compensated advanced chronic liver disease. • The LPC was a significant predictor for transplant-free survival in patients with compensated and those with decompensated advanced chronic liver disease.

A clinical observation of recurrent painful ophthalmoplegic neuropathy in a 49-year old female patient

Recurrent painful ophthalmoplegic neuropathy is a rare clinical syndrome, included into the International Classification of Headache Disorders, 3rd edition, and characterized by recurrent headache episodes associated with dysfunction of one or more ocular motor nerves. The diagnosis implies mandatory exclusion of other potential causes of the symptoms. The paper presents a clinical case of a 49-year old female patient with a unilateral headache paroxysms and subsequent paresis of the oculomotor nerve. A thorough clinical assessment, with neuroimaging (brain magnetic resonance imaging and magnetic resonance angiography of arteries and veins of the head) showed no morphological abnormalities that could cause clinical symptoms of oculomotor neuropathy. Her past history was remarkable for a similar episode of prolonged unilateral headache and oculomotor paresis about 10 years before, which were at that time considered as an acute cerebrovascular accident. Thus, two episodes of similar clinical signs of painful neuropathy, the absence of other possible causes of the disease, and the complete regression of symptoms after treatment (ibuprofen and gabapentin) confirmed the diagnosis of recurrent painful ophthalmoplegic neuropathy.