Real-time dual-modal vein imaging system

Visualization of Subcutaneous Blood Vessels Based on Hyperspectral Imaging and Three-Wavelength Index Images

Blood vessel visualization technology allows nursing staff to transition from traditional palpation or touch to locate the subcutaneous blood vessels to visualized localization by providing a clear visual aid for performing various medical procedures accurately and efficiently involving blood vessels; this can further improve the first-attempt puncture success rate for nursing staff and reduce the pain of patients. We propose a novel technique for hyperspectral visualization of blood vessels in human skin. An experiment with six participants with different skin types, race, and nationality backgrounds is described. A mere separation of spectral layers for different skin types is shown to be insufficient. The use of three-wavelength indices in imaging has shown a significant improvement in the quality of results compared to using only two-wavelength indices. This improvement can be attributed to an increase in the contrast ratio, which can be as high as 25%. We propose and implement a technique for finding new index formulae based on an exhaustive search and a binary blood-vessel image obtained through an expert assessment. As a result of the search, a novel index formula was deduced, allowing high-contrast blood vessel images to be generated for any skin type.

Multispectral Imaging for Vein Localization and Contrast Enhancement

Intravenous (IV) catheterization is a common procedure. Still, there is a 26% chance of the first attempt catheterization failure due to the changing visibility of veins because of the patient's skin tone and body fat content. Ultrasound assistive devices help locate deeper veins but are not practical in emergencies, and transillumination assistive devices have a low field of view. Commercial near-infrared (NIR) imaging devices are effective in vein localization but are expensive and are not used in low-cost clinical settings. To overcome this, NIR Multispectral Imaging (MSI) was used to find the optimal wavelength that provides the enhanced visualization of veins for all skin types and Body Mass Index (BMI). The band with the highest vein-to-skin contrast ratio was selected and contrast enhancement was done using our proposed method. The primary blocks of the proposed method are Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE), Adaptive Thresholding, and image Fusion. The optimal spectral range was found to be 814-876 nm and our method increased the contrast by 0.41, 0.375, and 0.39 for fair, brown, and dark brown skin types, respectively, with different BMI.Clinical relevance- From the study, we can develop a potentially low-cost vein localization assistive device for training medical and nursing students and use it in emergencies for venous access to improve confidence in IV catheterization.

Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches

Venepuncture is one of the most crucial processes in many medical procedures. However, finding a real-time and vibrant visualization of the vein structures faces many difficulties. Several devices were introduced to solve this problem, yet, these devices shared common drawbacks, primarily when visualizing deep veins or veins in a thicker tissue of the human body. This study proposes a novel method for visualizing vein structures using a near-infrared (NIR) imaging technique enhanced with Hessian ridge detection. Several factors, including the wavelength of NIR light, square LED and ring LED arrangement and the effect of the diffuser and number of LEDs, were evaluated in the study. This study improves the overall quality of the acquired vein images and highlights the vein-morphological structure through image processing techniques. The study’s main aim is to achieve the highest number of visible veins. Based on the optical window, the maximum absorption range in the NIR spectrum was found from 700 to 950 nm. The NIR light absorption of human deoxygenated blood in the vein was highest at 850 nm peak of wavelength. The image processing further enhances the vein image by highlighting the extracted vein. The study also suggests that the square LED arrangements of NIR illumination are much more robust than the ring LED arrangement in ensuring excellent light penetration. The light diffuser further adds promising effects to the NIR illumination process. In terms of the square LED arrangement, increasing the square LED for enlarging the illumination area did not show any degradation effects in the visualization process. Overall, this paper presents an integrated hardware and software solution for the NIR image acquisition of a vein visualization system to cope with the image visualization of the vein for a thicker part of the human tissue, particularly on the arm and palm area.

U-DAVIS-Deep Learning Based Arm Venous Image Segmentation Technique for Venipuncture

Arm Venous Segmentation plays a crucial role in smart venipuncture. The difficulties faced in locating veins for intravenous procedures can be diminished using computer vision for vein imaging. To facilitate this, a high-resolution dataset consisting of arm images was curated and has been presented in this study. Leveraging the ability of Near Infrared Imaging to easily detect veins, ambient lighting conditions were created inside a small enclosure to capture the images. The acquired images were annotated to create the corresponding masks for the dataset. To extend the scope and assert the usability of the dataset, the images, and corresponding masks were used to train an image segmentation model. In addition to using basic preprocessing and image augmentation based techniques, a U-Net based algorithmic architecture has been used to facilitate the task of segmentation. Subsequently, the results of performing image segmentation after applying the preprocessing methods have been compared using various evaluation metrics and have been visualised in the study. Furthermore, the possible applications of the presented dataset have been investigated in the study.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

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.

Three-dimensional imaging through turbid media using deep learning: NIR transillumination imaging of animal bodies

Using near-infrared (NIR) light with 700-1200 nm wavelength, transillumination images of small animals and thin parts of a human body such as a hand or foot can be obtained. They are two-dimensional (2D) images of internal absorbing structures in a turbid medium. A three-dimensional (3D) see-through image is obtainable if one can identify the depth of each part of the structure in the 2D image. Nevertheless, the obtained transillumination images are blurred severely because of the strong scattering in the turbid medium. Moreover, ascertaining the structure depth from a 2D transillumination image is difficult. To overcome these shortcomings, we have developed a new technique using deep learning principles. A fully convolutional network (FCN) was trained with 5,000 training pairs of clear and blurred images. Also, a convolutional neural network (CNN) was trained with 42,000 training pairs of blurred images and corresponding depths in a turbid medium. Numerous training images were provided by the convolution with a point spread function derived from diffusion approximation to the radiative transport equation. The validity of the proposed technique was confirmed through simulation. Experiments demonstrated its applicability. This technique can provide a new tool for the NIR imaging of animal bodies and biometric authentication of a human body.

Competitive Real-Time Near Infrared (NIR) Vein Finder Imaging Device to Improve Peripheral Subcutaneous Vein Selection in Venipuncture for Clinical Laboratory Testing

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.

Novel Multimodal, Multiscale Imaging System with Augmented Reality

A novel multimodal, multiscale imaging system with augmented reality capability were developed and characterized. The system offers 3D color reflectance imaging, 3D fluorescence imaging, and augmented reality in real time. Multiscale fluorescence imaging was enabled by developing and integrating an in vivo fiber-optic microscope. Real-time ultrasound-fluorescence multimodal imaging used optically tracked fiducial markers for registration. Tomographical data are also incorporated using optically tracked fiducial markers for registration. Furthermore, we characterized system performance and registration accuracy in a benchtop setting. The multiscale fluorescence imaging facilitated assessing the functional status of tissues, extending the minimal resolution of fluorescence imaging to ~17.5 µm. The system achieved a mean of Target Registration error of less than 2 mm for registering fluorescence images to ultrasound images and MRI-based 3D model, which is within clinically acceptable range. The low latency and high frame rate of the prototype system has shown the promise of applying the reported techniques in clinically relevant settings in the future.

FPGA-Based Two-Dimensional Matched Filter Design for Vein Imaging Systems

Venipuncture is a common medical procedure. The use of augmented reality-based assistive devices can improve the first puncture success rate in patients with poor vascular filling. In order to improve the image rendering quality and speed of auxiliary equipment, this study develop a two-dimensional matched filtering algorithm on a Field Programmable Gate Array (FPGA) in a near-infrared vein imaging system, which use parallel processing to offer real-time response and is designed as a small handheld portable device. A customized dorsal hand vein image library with 200 images captured from 120 participants is used to analyze the effects of convolution kernel parameters and exposure time on vascular imaging with different depths, and the correlation model between these parameters and vascular depth are constructed. We use the Tenengrad, variance, Laplace smoothness and standard deviation as evaluation indicators, and compare our algorithm with three other related studies. Experimental results show that the rendering quality of our proposed algorithm is significantly higher than other algorithms. In addition, the rendering speed of our algorithm can reach 66 fps, which is twice faster than the current fastest algorithm.

Ultra-broadband near-infrared emission CuInS2/ZnS quantum dots with high power efficiency and stability for the theranostic applications of mini light-emitting diodes

Broadband near-infrared CuInS₂/ZnS quantum with up to 94.8% quantum yield was synthesized with fast precursor decomposition. The better power efficiency and stability of CuInS₂/ZnS mini-LED were performed with penetration tests and vein imaging.