Imaging Of Human Skin Research Articles

Quantitative multilayered assessment of skin lightening by photoacoustic microscopy.

With the emergence of various new skin-lightening products, there is an urgent need to scientifically evaluate the efficacy and toxicology of these products, and provide scientific guidance for their use based on physiological differences between individuals. Visualized imaging methods and quantitative evaluation criteria play key roles in evaluating the efficacy of skin-lightening products. In order to quantify the changes in the multilayered morphology and endogenous components of human skin before and after the use of lightening products, high-resolution three-dimensional (3D) imaging of human skin is required. In this study, photoacoustic microscopy (PAM; SSPM-532, Guangdong Photoacoustic Medical Technology Co., Ltd.) was used to capture the morphological structures of human skin and reveal skin components quantitatively. The efficacy and safety of skin-lightening products were evaluated by measuring skin melanin concentration and observing skin morphology. The melanin concentration in the epidermis was obtained by examining the linear relationship between photoacoustic (PA) signals. Further, the epidermal thickness and the melanin distribution were obtained in the cross-sectional (x-z) and lateral (x-y) images. Finally, the efficacy of skin-lightening products was evaluated according to the concentration and distribution of melanin in the epidermis, and the safety of cosmetics was assessed by observing the vascular morphology in the dermis. PAM noninvasively could assess the multilayered morphological structures of human skin, which allowed for quantification of epidermal thickness and melanin concentration of different skin sites. Based on this, the efficacy and safety of skin-lightening products in multilayer structures were quantitatively evaluated. As a quantitative imaging method, PAM, has the potential to accurately evaluate the use of skin-lightening products. The method can also be extended to assessments within the larger field of aesthetic medicine.

Quantitative and Anatomical Imaging of Human Skin by Noninvasive Photoacoustic Dermoscopy.

Imaging plays a vital role in the diagnosis and treatment of skin diseases. However, pure optical imaging technique is limited to the visualization of superficial skin tissues. Ultrasonic imaging technique can detect deep tissues, but it lacks detailed information on microscopic pathological structures. Photoacoustic imaging is an advanced technology that bridges the spatial-resolution gap between optical and ultrasonic techniques, by the modes of optical excitation and acoustic detection. Photoacoustic dermoscopy (PAD), based on photoacoustic technology, can noninvasively obtain high-resolution anatomical structures by endogenous absorbers, such as melanin, hemoglobin, lipids, etc. In the past years, PAD has gradually been developed in clinical dermatology for the diagnosis of melanoma, psoriasis, port-wine stains, dermatitis, skin grafting, and testing the efficacy of cosmetics. This protocol provides detailed procedures for PAD construction, including component selection, equipment setup, and system calibration. A step-by-step guide for human skin imaging is provided as an example application. Image reconstruction and troubleshooting procedures are also elaborated. PAD offers the 3D volumetric images of human skin, and quantitatively analyzes the vascular morphology in the dermis. The protocol will provide clinicians with standardized and reasonable guidance in dermatological imaging.

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases

The performance of a model in machine learning problems highly depends on the dataset and training algorithms. Choosing the right training algorithm can change the tale of a model. While some algorithms have a great performance in some datasets, they may fall into trouble in other datasets. Moreover, by adjusting hyperparameters of an algorithm, which controls the training processes, the performance can be improved. This study contributes a method to tune hyperparameters of machine learning algorithms using Grey Wolf Optimization (GWO) and Genetic algorithm (GA) metaheuristics. Also, 11 different algorithms including Averaged Perceptron, FastTree, FastForest, Light Gradient Boost Machine (LGBM), Limited memory Broyden Fletcher Goldfarb Shanno algorithm Maximum Entropy (LbfgsMxEnt), Linear Support Vector Machine (LinearSVM), and a Deep Neural Network (DNN) including four architectures are employed on 11 datasets in different biological, biomedical, and nature categories such as molecular interactions, cancer, clinical diagnosis, behavior related predictions, RGB images of human skin, and X-rays images of Covid19 and cardiomegaly patients. Our results show that in all trials, the performance of the training phases is improved. Also, GWO demonstrates a better performance with a p-value of 2.6E-5. Moreover, in most experiment cases of this study, the metaheuristic methods demonstrate better performance and faster convergence than Exhaustive Grid Search (EGS). The proposed method just receives a dataset as an input and suggests the best-explored algorithm with related arguments. So, it is appropriate for datasets with unknown distribution, machine learning algorithms with complex behavior, or users who are not experts in analytical statistics and data science algorithms.

Ultracompact fluorescence smartphone attachment using built-in optics for protoporphyrin-IX quantification in skin.

Smartphone-based fluorescence imaging systems have the potential to provide convenient quantitative image guidance at the point of care. However, common approaches have required the addition of complex optical attachments, which reduce translation potential. In this study, a simple clip-on attachment appropriate for fluorescence imaging of protoporphyrin-IX (PpIX) in skin was designed using the built-in light source and ultrawide camera sensor of a smartphone. Software control for image acquisition and quantitative analysis was developed using the 10-bit video capability of the phone. Optical performance was characterized using PpIX in liquid tissue phantoms and endogenously produced PpIX in mice and human skin. The proposed system achieves a very compact form factor (<30 cm3) and can be readily fabricated using widely available low-cost materials. The limit of detection of PpIX in optical phantoms was <10 nM, with good signal linearity from 10 to 1000 nM (R2 >0.99). Both murine and human skin imaging verified that in vivo PpIX fluorescence was detected within 1 hour of applying aminolevulinic acid (ALA) gel. This ultracompact handheld system for quantification of PpIX in skin is well-suited for dermatology clinical workflows. Due to its simplicity and form factor, the proposed system can be readily adapted for use with other smartphone devices and fluorescence imaging applications. Hardware design and software for the system is made freely available on GitHub (https://github.com/optmed/CompactFluorescenceCam).

LB816 Wavelet-based image enhancement of confocal data

Wavelet-based signal processing provides a powerful mechanism for image enhancement, while avoiding noise amplification. Fractional wavelet algorithms function as a family of filters that can be used for an array of image processing applications. Our objective was to employ fractional wavelet transforms (FRWT) for improving image quality and visibility of structures seen in confocal images of human skin. Images were acquired from deep dermal levels using reflectance confocal microscopy (RCM). The original images are very dark and have a low signal due to decreased reflectance of near infrared light in deeper levels.

Characterizing skin color before and after 100‐m sprinting

AbstractWe studied skin color in participants before and after a 100‐m sprint using a Konica Minolta CM‐2600d spectrophotometer. Four body positions (forehead, cheek, neck, and inner forearm) were measured for the approximately 30 students (non‐professional runners) who participated in the experiment. Skin reflectance was measured three times at each position before running and again soon afterward. Analysis of the data showed that average measurement repeatability (mean color difference from the mean [MCDM]) was 0.5, 0.4, 0.3, and 0.2 CIELAB units for forehead, cheek, neck, and inner forearm, respectively. However, average skin color differences produced by running were 1.8, 2.2, 2.0, and 1.7 CIELAB units for forehead, cheek, neck, and inner forearm positions, respectively, which are considerably higher than repeatability before or after running. Furthermore, appearance variation was analyzed in the CIELAB space, and it was found that sprinting for 100 m considerably changes the values of the red‐green a* coordinate, while the lightness L* and yellow‐blue b* coordinates remain almost constant. More specifically, on average, a* decreases after running for the forehead, cheek, and neck positions, while the opposite is true for the inner forearm position. The findings in this article may be useful to test previous physical models, achieve more realistic images of human skin after sprinting, and contribute to work of CIE TC 1‐92 on skin spectra database.

Three-dimensional acoustic impedance mapping of human skin by improved time–frequency domain analysis

Three-dimensional acoustic impedance microscopy was proposed for human cheek skin observation. A focused ultrasound wave was transmitted through a polymer substrate in contact with the skin, and the reflection was acquired by a time-domain measurement. The reflection was compared with that from a reference material representing the impulse response of the system. Time–frequency (dual-domain) deconvolution was performed to calculate the time-dependent reflection coefficient, which can subsequently be interpreted as the local acoustic impedance along the depth. The optimal conditions for deconvolution were discussed in order to perform quick and stable signal processing. Moreover, it was found that the stability of the dual-domain deconvolution was improved by adding the DC component to the transfer matrix, leading to a more stable calculation of the acoustic impedance distribution. The stability was statistically evaluated by means of the intensity distribution of the deconvolved signal. Finally, a 3D human cheek skin image was successfully reconstructed.

Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation.

The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentation. However, a gigantic amount of annotated data is required for the successful training of deep learning models, which demands a great deal of effort and is costly. To overcome this fundamental problem, an automatic simulation algorithm to generate OCT-like skin image data with augmented capillary networks (ACNs) in a three-dimensional volume (which we called the ACN data) is presented. This algorithm simultaneously acquires augmented FF-OCT and corresponding ground truth images of capillary structures, in which potential functions are introduced to conduct the capillary pathways, and the two-dimensional Gaussian function is utilized to mimic the brightness reflected by capillary blood flow seen in real OCT data. To assess the quality of the ACN data, a U-Net deep learning model was trained by the ACN data and then tested on real in vivo FF-OCT human skin images for capillary segmentation. With properly designed data binarization for predicted image frames, the testing result of real FF-OCT data with respect to the ground truth achieved high scores in performance metrics. This demonstrates that the proposed algorithm is capable of generating ACN data that can imitate real FF-OCT skin images of capillary networks for use in research and deep learning, and that the model for capillary segmentation could be of wide benefit in clinical and biomedical applications.

Imaging human skin autograft integration with optical coherence tomography.

Skin autografting is a common clinical procedure for reconstructive surgery. Despite its widespread use, very few studies have been conducted to non-invasively evaluate and monitor the vascular and structural features of skin grafts. This study, therefore, aims to demonstrate the potential of optical coherence tomography (OCT) alongside OCT-based angiography (OCTA) to non-invasively image and monitor human skin graft health and integration over time. An in-house-built clinical prototype OCT system was used to acquire OCT/OCTA images from patients who underwent split-thickness skin graft surgery following severe burn damage to the skin. The OCT imaging was carried out at multiple locations over multiple time points with a field of view of ~9 mm × 9 mm and a penetration depth of ~1.5 mm. In addition to obtaining high-resolution qualitative images, we also quantitatively measured and compared specific structural and vascular parameters, such as identifiable layer thickness and corresponding vascular area density and diameter. Two patients (patient #1 and #2) were enrolled for this preliminary study. Vascular and structural features were successfully imaged and measured in the graft tissue and integration layer immediately beneath at different time points. Revascularization, healing, and integration were monitored with patient-specific details. Results of the quantitative image analysis from patient #1 indicated that integration layer thickness 16-day post-surgery was significantly less (~50%) than that of 7-day post-surgery. Additionally, with patient #2, significant growth (~20%) was seen with the vascular area density of both the graft and corresponding integration layer beneath between 6 and 14 days post-surgery. Our preliminary studies show that OCT/OCTA has clinical potential to image and measure numerous features of human skin graft health and integration in the days and weeks following split-thickness surgery. For the first time, we demonstrate the applicability of non-invasive imaging technology for novel clinical uses that could eventually aid in the betterment of surgical practices and clinical outcomes.

Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography.

A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previously used for resolution enhancement of photography and optical microscopy images. We have adapted and improved this technique for OCT image generation. Conditional GANs (cGANs) were trained on a novel set of ultrahigh resolution spectral domain OCT volumes, termed micro-OCT, as the high-resolution ground truth (∼1 μm isotropic resolution). The ground truth was paired with a low-resolution image obtained by synthetically degrading resolution 4x in one of (1-D) or both axial and lateral axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo imaging of human labial (lip) tissue and mouse skin were used in separate feasibility experiments. Accuracy of resolution enhancement compared to ground truth was quantified with human perceptual accuracy tests performed by an OCT expert. The GAN loss in the optimization objective, noise injection in both the generator and discriminator models, and multi-scale discrimination were found to be important for achieving realistic speckle appearance in the generated OCT images. The utility of high-resolution speckle recovery was illustrated by an example of micro-OCT imaging of blood vessels in lip tissue. Qualitative examples applying the models to image data from outside of the training data distribution, namely human retina and mouse bladder, were also demonstrated, suggesting potential for cross-domain transferability. This preliminary study suggests that deep learning generative models trained on OCT images from high-performance prototype systems may have potential in enhancing lower resolution data from mainstream/commercial systems, thereby bringing cutting-edge technology to the masses at low cost.

Reflection-mode virtual histology using photoacoustic remote sensing microscopy

Histological visualizations are critical to clinical disease management and are fundamental to biological understanding. However, current approaches that rely on bright-field microscopy require extensive tissue preparation prior to imaging. These processes are both labor intensive and contribute to creating significant delays in clinical feedback for treatment decisions that can extend to 2–3 weeks for standard paraffin-embedded tissue preparation and interpretation, especially if ancillary testing is needed. Here, we present the first comprehensive study on the broad application of a novel label-free reflection-mode imaging modality known as photoacoustic remote sensing (PARS) for visualizing salient subcellular structures from various common histopathological tissue preparations and for use in unprocessed freshly resected tissues. The PARS modality permits non-contact visualizations of intrinsic endogenous optical absorption contrast to be extracted from thick and opaque biological targets with optical resolution. The technique was examined both as a rapid assessment tool that is capable of managing large samples (> 1 cm2) in under 10 min, and as a high contrast imaging modality capable of extracting specific biological contrast to simulate conventional histological stains such as hematoxylin and eosin (H&E). The capabilities of the proposed method are demonstrated in a variety of human tissue preparations including formalin-fixed paraffin-embedded tissue blocks and unstained slides sectioned from these blocks, including normal and neoplastic human brain, and breast epithelium involved with breast cancer. Similarly, PARS images of human skin prepared by frozen section clearly demonstrated basal cell carcinoma and normal human skin tissue. Finally, we imaged unprocessed murine kidney and achieved histologically relevant subcellular morphology in fresh tissue. This represents a vital step towards an effective real-time clinical microscope that overcomes the limitations of standard histopathologic tissue preparations and enables real-time pathology assessment.

Fast, large area multiphoton exoscope (FLAME) for macroscopic imaging with microscopic resolution of human skin

We introduce a compact, fast large area multiphoton exoscope (FLAME) system with enhanced molecular contrast for macroscopic imaging of human skin with microscopic resolution. A versatile imaging platform, FLAME combines optical and mechanical scanning mechanisms with deep learning image restoration to produce depth-resolved images that encompass sub-mm2 to cm2 scale areas of tissue within minutes and provide means for a comprehensive analysis of live or resected thick human skin tissue. The FLAME imaging platform, which expands on a design recently introduced by our group, also features time-resolved single photon counting detection to uniquely allow fast discrimination and 3D virtual staining of melanin. We demonstrate its performance and utility by fast ex vivo and in vivo imaging of human skin. With the ability to provide rapid access to depth resolved images of skin over cm2 area and to generate 3D distribution maps of key sub-cellular skin components such as melanocytic dendrites and melanin, FLAME is ready to be translated into a clinical imaging tool for enhancing diagnosis accuracy, guiding therapy and understanding skin biology.

Elemental and molecular imaging of human full thickness skin after exposure to heavy metals.

Compelling evidence suggests that heavy metals have potentially harmful effects on the skin. However, knowledge about cellular signaling events and toxicity subsequent to human skin cell exposure to metals is still poorly documented. The aim of this study was to focus on the interaction between four different heavy metals (lead, nickel, cadmium, and mercury) at doses mimicking chronic low-levels of environmental exposure and the effect on skin to get better insight into metal-cell interactions. We provide evidence that the two metals (lead and nickel) can permeate the skin and accumulate at high concentrations in the dermis. The skin barrier was disrupted after metal exposure and this was accompanied by apoptosis, DNA damage and lipid oxidation. Skin antioxidant enzymes such as glutathione peroxidase and methionine sulfoxide reductase are also heavy metal targets. Taken together, our findings provide insight into potential mechanisms of metal-induced oxidative stress production and the cellular consequences of these events.

General method for classification of fiber families in fiber-reinforced materials: application to in-vivo human skin images

Fiber structures play a major role for the function of fiber-reinforced materials such as biological tissue. An objective classification of the fiber orientations into fiber families is crucial to understand its mechanical properties. We introduce the Fiber Image Network Evaluation Algorithm (FINE algorithm) to classify and quantify the number of fiber families in scientific images. Each fiber family is characterized by an amplitude, a mean orientation, and a dispersion. A new alignment index giving the averaged fraction of aligned fibers is defined. The FINE algorithm is validated by realistic grayscale Monte-Carlo fiber images. We apply the algorithm to an in-vivo depth scan of second harmonic generation images of dermal collagen in human skin. The derived alignment index exhibits a crossover at a critical depth where two fiber families with a perpendicular orientation around the main tension line arise. This strongly suggests the presence of a transition from the papillary to the reticular dermis. Hence, the FINE algorithm provides a valuable tool for a reliable classification and a meaningful interpretation of in-vivo collagen fiber networks and general fiber reinforced materials.

Rapid functional optoacoustic micro-angiography in a burst mode.

Optoacoustic microscopy (OAM) can image intrinsic optical absorption contrast at depths of several millimeters where state-of-the-art optical microscopy techniques fail due to intense light scattering in living tissues. Yet, wide adoption of OAM in biology and medicine is hindered by slow image acquisition speed, small field of view (FOV), and/or lack of spectral differentiation capacity of common system implementations. We report on a rapid acquisition functional optoacoustic micro-angiography approach that employs a burst-mode laser triggering scheme to simultaneously acquire multi-wavelength 3D images over an extended FOV covering ${50}\;{\rm mm} \times {50}\;{\rm mm}$50mm×50mm in a single mechanical overfly scan, attaining 28 µm and 14 µm resolution in lateral and axial dimensions, respectively. Owing to an ultrawideband low-noise design featuring a spherically focused polyvinylidene difluoride transducer, we demonstrate imaging of human skin and underlying vasculature at up to 3.8 mm depth when using per-pulse laser energies of only 25 µJ without employing signal averaging. Overall, the developed system greatly enhances performance and usability of OAM for dermatologic and micro-angiographic studies.

Mirau-based line-field confocal optical coherence tomography.

Line-field confocal optical coherence tomography (LC-OCT) is an imaging technique in which A-scans are acquired in parallel through line illumination with a broadband laser and line detection with a line-scan camera. B-scan imaging at high spatial resolution is achieved by dynamic focusing in a Linnik interferometer. This paper presents an LC-OCT device based on a custom-designed Mirau interferometer that offers similar spatial resolution and detection sensitivity. The device has the advantage of being more compact and lighter. In vivo imaging of human skin with a resolution of 1.3 µm × 1.1 µm (lateral × axial) is demonstrated over a field of 0.9 mm × 0.4 mm (lateral × axial) at 12 frames per second.

Robust three-dimensional registration on optical coherence tomography angiography for speckle reduction and visualization.

In the clinical applications of optical coherence tomography angiography (OCTA), the repeated scanning and averaging method can provide better contrast with reduced speckle noises in the final results, which are useful for visualizing and quantifying vascular components with high accuracy, reproducibility, and reliability. However, the inevitable patient motion presents a challenge to this method. The objective of this study is to meet this challenge by introducing a 3D registration method to register optical coherence tomography (OCT)/OCTA scans for precise volume averaging of multiple scans to improve the signal-to-noise ratio (SNR) and increase quantification accuracy. The proposed method utilized both rigid affine transformation and non-rigid B-spline transformation in which their parameters were optimized and calculated by the average stochastic gradient descent on OCT structural images. In addition, we also introduced a multi-level resolution approach to further improve the robustness and computational speed of our proposed method. The imaging performance was tested on in vivo imaging of human skin and eye and assessed by SNR, peak signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC). Five subjects were enrolled in this study for obtaining in vivo images of skin and retina. The proposed registration and averaging method provided substantial improvements of the imaging performance in terms of vessel connectivity and signal to noise ratio. The increase of repeated volume numbers in the averaging improves all the metrics assessed, i.e., SNR, PSNR and NCC. An improvement of the SNR from 10 to 40 dB after 10 repeated volumetric averaging was achieved. The proposed 3D registration and averaging method is effective in reducing speckle noises and suppressing motion artifacts, thereby improving SNR, PSNR and NCC metrics for final averaged images. It is expected that the proposed algorithm would be practically useful in better visualization and more reliable quantification of in vivo OCT and OCTA data, which would be beneficial to OCT clinical applications.

Dual-mode line-field confocal optical coherence tomography for ultrahigh-resolution vertical and horizontal section imaging of human skin in vivo.

Line-field confocal optical coherence tomography (LC-OCT) is a recently introduced technique for ultrahigh-resolution vertical section (B-scan) imaging of human skin in vivo. This work presents a new implementation of the LC-OCT technique to obtain horizontal section images (C-scans) in addition to B-scans. C-scan imaging is achieved with this dual-mode LC-OCT system using a mirror galvanometer for lateral scanning along with a piezoelectric chip for modulation of the interferometric signal. A quasi-identical spatial resolution of ∼ 1 µm is measured for both B-scans and C-scans. The images are acquired in both modes at a rate of 10 frames per second. The horizontal field of view of the C-scans is 1.2 × 0.5 mm2, identical to the vertical field of view of the B-scans. The user can switch between the two modes by clicking a button. In vivo cellular-resolution imaging of human skin is demonstrated in both B-scan and C-scan modes, with the possibility to navigate within the skin tissues in real time.

Design of a Compact V-Band Transceiver and Antenna for Millimeter-Wave Imaging Systems

In this paper, we introduce a synthetic aperture radar (SAR)-based millimeter-wave imaging system for high-resolution imaging of human skin, in addition to traditional nondestructive testing applications. The imaging system, which operates in the 58–64-GHz frequency range, forms the basic building block of a future millimeter-wave camera suitable for imaging burned skin and skin cancers. The system is composed of a coherent transceiver and two antennas, principally enabling a quasi-monostatic measurement configuration. The designed antenna is an antipodal Vivaldi antenna with 5.0–8.0-dB gain and half-power beamwidths of ~79.0° and ~76.0°in its $E$ – and $H$ -planes, respectively. The transceiver is designed using a complete system-on-chip transmitter and receiver capable of $I/Q$ detection. The fully designed imaging system has 47 dB of dynamic range and is phase-calibrated using a robust wideband calibration procedure based on single-frequency SAR imaging approach. Examples of images produced with this system include burned pigskin skin and a small balsa wood panel with four small embedded rubber pieces.