Non-contact Imaging Technique Research Articles

Automated hyperspectral imaging for non-invasive characterization of human eye vasculature: A potential tool for ocular vascular evaluation

The vascular supply to the human eye plays a vital role in maintaining ocular health, making its non-invasive evaluation essential for diagnosing and managing various ocular disorders. This paper presents a novel approach utilizing hyperspectral imaging (HSI) to non-invasively characterize human eye vasculature. The proposed system aims to specifically identify the blood atrium and veins of the human eye at 470 nm and 750 nm, respectively, using quantitative phase analysis and k-means clustering. The study involved capturing diffused reflection spectra and hyperspectral images of the human eye at different wavelengths to reveal distinctive vascular features. The results of ten volunteers demonstrate promising capabilities in automated differentiation of atrium and veins, as well as the potential for mapping varicose veins in the lower limb. This non-invasive and non-contact imaging technique shows great promise in facilitating accurate and detailed evaluation of ocular blood flow, providing valuable information for clinical diagnosis and treatment in ophthalmology and vascular medicine fields.

Evolutionary Gravitational Neocognitron Neural Network-Based Blood Flow Velocity Prediction Using Multi-Exposure Laser Speckle Contrast Imaging

Wide-field and noncontact imaging technique called laser speckle contrast imaging (LSCI) is utilized to map blood flow. To greatly improve prediction accuracy, the block-matching along 3-dimensional transform domain collaborative filtering-dependent denoising technique is developed. To implement real-time denoising, the processing time makes complexity. Due to the existence of considerable noise and artifacts, this is challenging to achieve the acceptable level with less raw speckle images. Notwithstanding, it acts poorly while learning from the original LSCI speckle contrast images because of the uneven noise distribution. To overcome this issue, an evolutionary gravitational neocognitron neural network-based blood flow velocity prediction using multiple exposure laser speckle contrast imaging (EGNNN-BFVP-MeLSCI) is proposed. Initially, the input MeLSCI datasets are collected from real-time dataset. The input picture is enhanced and noise is removed using pre-processing technique called altered phase preserving dynamic range compression (APPDRC). Gray level co-occurrence matrix (GLCM) window adaptive approach-dependent feature extraction approach is used for the preprocessed pictures. Using GLCM window adaptive method, the picture characteristics, such as intensity information, images derivative, geodesic information, contrasts, energy, correlations, homogeneity, and entropy, are retrieved. Then, the extracted features are transferred into the EGNNN classifier for accurately predicting the blood flow velocity. The performance of proposed method is executed at python and evaluated under certain performance metrics, such as accuracy, precision, sensitivity, specificity, [Formula: see text]-measure, ROC, computation time, mean squared error, root mean squared error, predicted velocity. The proposed EGNNN-BFVP-MeLSCI method attains higher accuracy of 96.31%, 97.06%, and 93.52%, lower computational time of 97.22%, 91.39%, 96.41% compared with existing approaches, like DnCNN-BFVP-MeLSCI, CFD-BFVP-MeLSCI, DLS-BFVP-MeLSCI, CNN-BFVP-MeLSCI, DBN-BFVP-MeLSCI, CGAN-BFVP-MeLSCI and MVN-BFVP-MeLSCI, respectively.

Real-time laser speckle contrast imaging measurement during normothermic machine perfusion in pretransplant kidney assessment.

Normothermic machine perfusion (NMP) provides a platform for pre-transplant kidney quality assessment that is essential for the use of marginal donor kidneys. Laser speckle contrast imaging (LSCI) presents distinct advantages as a real-time and noncontact imaging technique for measuring microcirculation. In this study, we aimed to assess the value of LSCI in visualizing renal cortical perfusion and investigate the additional value of dual-side LSCI measurements compared to single aspect measurement during NMP. Porcine kidneys were obtained from a slaughterhouse and then underwent NMP. LSCI was used to measure one-sided cortical perfusion in the first 100 min of NMP. Thereafter, the inferior renal artery branch was occluded to induce partial ischemia and LSCI measurements on both ventral and dorsal sides were performed. LSCI fluxes correlated linearly with the renal blood flow (R2 = 0.90, p < 0.001). After renal artery branch occlusion, absence of renal cortical perfusion could be visualized and semiquantified by LSCI. The overall ischemic area percentage of the ventral and dorsal sides was comparable (medianinterquartile range[IQR], 38 [24-43]% vs. 29 [17-46]%, p = 0.43), but heterogenous patterns between the two aspects were observed. There was a significant difference in oxygen consumption (mean ± standard deviation [SD], 2.57 ± 0.63 vs. 1.83 ± 0.49 mLO2 /min/100 g, p < 0.001), urine output (median [IQR], 1.3 [1.1-1.7] vs. 0.8 [0.6-1.3] mL/min, p < 0.05), lactate dehydrogenase (mean ± SD, 768 ± 370 vs. 905 ± 401 U/L, p < 0.05) and AST (mean ± SD, 352 ± 285 vs. 462 ± 383 U/L, p < 0.01) before and after renal artery occlusion, while no significant difference was found in creatinine clearance, fractional excretion of sodium, total sodium reabsorption and histological damage. LSCI fluxes correlated linearly with renal blood flow during NMP. Renal cortical microcirculation and absent perfusion can be visualized and semiquantified by LSCI. It provides a relative understanding of perfusion levels, allowing for a qualitative comparison between regions in the kidney. Dual-side LSCI measurements are of added value compared to single aspect measurement and renal function markers.

Microsphere-assisted enhanced photothermal lens detection integrated with digital holographic microscopy for 3D particle sensing and thermal diffusivity measurement

In this paper, we show that by extending the concept of “microsphere-assisted imaging” into thermal lens (TL) detection, enhancement of photothermal detection is achieved. Furthermore, by integration of TL detection and digital holographic microscopy (DHM), we provide simultaneous measurement of photothermal properties as well as complementary imaging of the sample. Simultaneous acquisition of photothermal properties and imaging is essential for characterization of a variety of samples in biochemistry, material science, and process technologies. TL is a sensitive methodology to detect low concentrations of analytes in low-loss samples, and DHM in the transmission mode is an effective technique for label-free, non-contact, and real-time imaging and measurement of phase objects, such as biomaterials. We demonstrate the usefulness of the TL-DHM integrated system by applying it for acquisition of photothermal maps of samples as well as obtaining their morphometrical information. Additionally, we measure the thermal diffusivity of polydimethylsiloxane because of the focusing effect of polystyrene microspheres. The system has the potential to be applied for a variety of objects including bio-samples and may be proposed as a bench-top characterization device.

Spatial frequency domain imaging for the assessment of scleroderma skin involvement.

Systemic sclerosis (SSc) is an autoimmune disease characterized by the widespread deposition of excess collagen in the skin and internal organs, as well as vascular dysfunction. The current standard of care technique used to quantify the extent of skin fibrosis in SSc patients is the modified Rodnan skin score (mRSS), which is an assessment of skin thickness based on clinical palpation. Despite being considered the gold standard, mRSS testing requires a trained physician and suffers from high inter-observer variability. In this study, we evaluated the use of spatial frequency domain imaging (SFDI) as a more quantitative and reliable method for assessing skin fibrosis in SSc patients. SFDI is a wide-field and non-contact imaging technique that utilizes spatially modulated light to generate a map of optical properties in biological tissue. The SFDI data were collected at six measurement sites (left and right forearms, hands, and fingers) of eight control subjects and ten SSc patients. mRSS were assessed by a physician, and skin biopsies were collected from subject's forearms and used to assess for markers of skin fibrosis. Our results indicate that SFDI is sensitive to skin changes even at an early stage, as we found a significant difference in the measured optical scattering () between healthy controls and SSc patients with a local mRSS score of zero (no appreciable skin fibrosis by gold standard). Furthermore, we found a strong correlation between the diffuse reflectance (Rd) at a spatial frequency of 0.2 mm-1 and the total mRSS between all subjects (Spearman correlation coefficient = -0.73, p-value < 0.0028), as well as high correlation with histology results. The healthy volunteer results show excellent inter- and intra-observer reliability (ICC > 0.8). Our results suggest that the measurement of tissue and Rd at specific spatial frequencies and wavelengths can provide an objective and quantitative assessment of skin involvement in SSc patients, which could greatly improve the accuracy and efficiency of monitoring disease progression and evaluating drug efficacy.

Imaging Cultural Heritage at Different Scales: Part I, the Micro-Scale (Manufacts)

Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties.

InfraRed Thermography and 3D-Data Fusion for Architectural Heritage: A Scoping Review

Comprehensive documentation is the foundation of effective conservation, repair and maintenance (CRM) practices for architectural heritage. In order to diagnose historic buildings and inform decision making, a combination of multi-disciplinary surveys is fundamental to understanding a building’s heritage and performance. Infrared thermography (IRT), a non-contact, non-invasive and non-destructive imaging technique, allows both qualitative and quantitative assessments of temperature to be undertaken. However, the inherent low spatial resolution of thermal imaging has led recent work to fuse thermographic and geometric data for the accurate 3D documentation of architectural heritage. This paper maps the scope of this emerging field to understand the application of IRT and 3D-data fusion (IRT-3DDF) for architectural heritage. A scoping review is undertaken to systematically map the current literature and determine research gaps and future trends. Results indicate that the increasing availability of thermal cameras and advances in photogrammetric software are enabling thermal models to be generated successfully for the diagnosis and holistic management of architectural heritage. In addition, it is evident that IRT-3DDF provides several opportunities for additional data integration, historic building information modelling (H-BIM) and temporal analysis of historic buildings. Future developments are needed to transform IRT-3DDF findings into actionable insights and to apply IRT-3DDF to pressing climate-related challenges, such as energy efficiency, retrofitting and thermal comfort assessments.

Suppressing artifacts in the total focusing method using the directivity of laser ultrasound

Based on a synthesized laser ultrasonic array, full matrix capture can be used to acquire data, which can then be post-processed using the total focusing method. However, this noncontact ultrasonic imaging technique has not been widely used because of the numerous artifacts in ultrasonic images and time-consuming data acquisition. To address these issues, this study proposes a post-processing algorithm, which uses the laser ultrasound directivity information to suppress the artifacts in the total focusing method’s images. In particular, a weight factor is defined using the directivity information. By multiplying the image intensity of the total focusing method with this factor, the algorithm uses not only the amplitude and phase information of laser ultrasound but also its directivity information. The experimental results indicate that four types of artifacts are suppressed. Because the grating lobe artifacts can be suppressed, a larger element spacing can be used to reduce the data acquisition time.

Using biospeckle and LIBS techniques with artificial intelligence to monitor phthalocyanine-gold nanoconjugates as a new drug delivery mediator for in vivo PDT

Photodynamic therapy (PDT) is a promising treatment method that generates reactive oxygen species by exciting a photosensitizer in the presence of light. The positive treatment outcome of PDT is enhanced by the photosensitizer’s ability to get out quickly from normal tissue. This study non-destructively evaluated the biocompatibility of novel synthesized phthalocyanine-gold nanoconjugates (Pc-Au NCs) as a drug delivery mediator for in vivo PDT. We first proposed a novel noncontact and safe biospeckle optical imaging technique to visualize the quantitative changes in the internal dynamics of biological tissue in response to PDT treatment. Second, laser-induced breakdown spectroscopy (LIBS) was utilized to emphasize the accumulation of gold nanoparticles (Au NPs) within the normal and cancerous tissue, which in turn verifies the cytotoxic and treatment effects of the Pc-Au NCs. The biospeckle images and LIBS spectra were obtained from tumor and normal tissues of male BALB/c malignancy-bearing mice before and after exposure to PDT. Then, they were statistically treated via different artificial intelligence techniques. For biospeckle analysis, the proposed method was established from the combination of second and higher-order statistical features extracted from the gray-level co-occurrence matrix (GLCM) and local contrast analysis (LCA), respectively, through principal component analysis (PCA). Regarding LIBS analysis, the powerful, most popular ensemble sequential learning algorithm Adaptive Boost was implemented on highly uncorrelated variables extracted from the raw spectra. The results, confirmed by the histological investigation, indicated the potential of the biospeckle and LIBS statistical properties in demonstrating the efficiency of the Pc-Au NCs as a drug delivery mediator for in vivo PDT.

A fusion denoising method based on homomorphic transform and 3D transform-domain collaborative filtering for laser speckle imaging of blood flow

Laser speckle contrast imaging (LSCI) is a real-time, full-field, non-contact optical imaging technique that has been widely used in blood flow imaging. However, its practical applications are limited due to its low signal-to-noise ratio (SNR) and low image contrast. In this study, a fusion denoising algorithm based on homomorphic transform and block-matching and 3D transform-domain collaborative filtering algorithm is proposed to reduce noise and improve image contrast. The reliability and applicability of the proposed method were evaluated by a phantom experiment and two in vivo experiments. The results show that the proposed algorithm can effectively remove the noise and enhance the image contrast. Consequently, the SNR is increased, the dynamic range of LSCI is expanded, and the estimation accuracy of blood flow is improved.

Convolutional neural network exploiting pixel surroundings to reveal hidden features in artwork NIR reflectograms

Near-infrared reflectography (NIR) is a well-established non-invasive and non-contact imaging technique. The NIR methods are able to reveal concealed layers of artwork, such as a painter’s sketch or repainted canvas. The information obtained may be helpful to historians for studying artist technique, attributing an artwork reconstructing faded details. Our research presents the improved method previously developed that reveals the hidden features by removing the information content of the visible spectrum from NIR. Based on convolutional neural networks (CNN), our model estimates the transfer function from visible spectra to NIR, which is nonlinear and specific for painting materials. Its parameters are learnt for particular paintings on the subsamples randomly selected across the canvas, and the model is further utilised to enhance the whole artwork. In addition to the previously developed model, our algorithm exploits each pixel’s surroundings to estimate its NIR response. This leads to more precise results and increased robustness to various noises. We demonstrate higher accuracy than the previous method on the historical paintings mock-ups and higher performance on well-known artworks such as Madonna dei Fusi attributed to Leonardo da Vinci.

Semiautomatic Toolmark Classification in Chased Metals Using Surface Microtopography

Chasing is one of the main decorative techniques found in metal work. The imprints left by different chasing tools are unique not only to the tool, but also to the chaser. The analysis and characterisation of toolmarks provide vast information that is valuable for the understanding of an object. Microtopography is a non-contact imaging technique that allows a metrical analysis of a surface on a microscopic scale. In this paper, metal mock-ups made using different chasing tools were scanned using microtopography, and their morphological features were used to classify the tools used for their fabrication. The mock-ups were artificially aged to replicate conditions of real archaeological metal objects. The overall classification accuracy obtained for the non-aged mock-ups was 91% for brass and 84% for copper mock-ups. After ageing, the classification accuracy was 65% for brass and 68% for copper. The change in classification accuracy before and after ageing was caused by the corrosion on the aged mock-ups. These results show that this is a promising methodology that could be applied on real chased metal objects.

In-vivo functional and structural retinal imaging using multiwavelength photoacoustic remote sensing microscopy

Many important eye diseases as well as systemic disorders manifest themselves in the retina. Retinal imaging technologies are rapidly growing and can provide ever-increasing amounts of information about the structure, function, and molecular composition of retinal tissue in-vivo. Photoacoustic remote sensing (PARS) is a novel imaging modality based on all-optical detection of photoacoustic signals, which makes it suitable for a wide range of medical applications. In this study, PARS is applied for in-vivo imaging of the retina and estimating oxygen saturation in the retinal vasculature. To our knowledge, this is the first time that a non-contact photoacoustic imaging technique is applied for in-vivo imaging of the retina. Here, optical coherence tomography is also used as a well-established retinal imaging technique to navigate the PARS imaging beams and demonstrate the capabilities of the optical imaging setup. The system is applied for in-vivo imaging of both microanatomy and the microvasculature of the retina. The developed system has the potential to advance the understanding of the ocular environment and to help in monitoring of ophthalmic diseases.

Optical coherence elastography for assessing the influence of intraocular pressure on elastic wave dispersion in the cornea

The cornea is a highly specialized organ that relies on its mechanical stiffness to maintain its aspheric geometry and refractive power, and corneal diseases such as keratoconus have been linked to abnormal tissue stiffness and biomechanics. Dynamic optical coherence elastography (OCE) is a clinically promising non-contact and non-destructive imaging technique that can provide measurements of corneal tissue stiffness directly in vivo. The method relies on the concepts of elastography where shear waves are generated and imaged within a tissue to obtain mechanical properties such as tissue stiffness. The accuracy of OCE-based measurements is ultimately dependent on the mathematical theories used to model wave behavior in the tissue of interest. In the cornea, elastic waves propagate as guided wave modes which are highly dispersive and can be mathematically complex to model. While recent groups have developed detailed theories for estimating corneal tissue properties from guided wave behavior, the effects of intraocular pressure (IOP)-induced prestress have not yet been considered. It is known that prestress alone can strongly influence wave behavior, in addition to the associated non-linear changes in tissue properties. This present study shows that failure to account for the effects of prestress may result in overestimations of the corneal shear moduli, particularly at high IOPs. We first examined the potential effects of IOP and IOP-induced prestress using a combination of approximate mathematical theories describing wave behavior in thin plates with observations made from data published in the OCE literature. Through wave dispersion analysis, we deduce that IOP introduces a tensile hoop stress and may also influence an elastic foundational effect that were observable in the low-frequency components of the dispersion curves. These effects were incorporated into recently developed models of wave behavior in nearly incompressible, transversely isotropic (NITI) materials. Fitting of the modified NITI model with ex vivo porcine corneal data demonstrated that incorporation of the effects of IOP resulted in reduced estimates of corneal shear moduli. We believe this demonstrates that overestimation of corneal stiffness occurs if IOP is not taken into consideration. Our work may be helpful in separating inherent corneal stiffness properties that are independent of IOP; changes in these properties and in IOP are distinct, clinically relevant issues that affect the cornea health.

Development and Performance Evaluation of Improved Clay Porous Pipes for Subsurface Irrigation

HighlightsClay and clay combined with zeolite was utilized for the production of clay pipes for subsurface micro-irrigation.Various modelling approaches are mathematically complex and require computational and technical skills, hence the need for other simpler approaches.Non-contact imaging and supervised classification (ArcGIS) techniques are utilized for wetting pattern study.The Plexiglas soil column experiment and incorporated techniques provide a visual understanding of soil-water movement interaction.Abstract. The increasing prominence of clay pipes in irrigation water application in drier regions and the importance of soil wetting pattern information requires a better understanding of subsurface irrigation system design and management. This article reported findings on two different porous clay pipes made up of 100% clay, and 25% zeolite as an additive to the 75% clay developed and produced. A new method was proposed to evaluate their performance. A non-contact thermal imaging technique and maximum likelihood supervised classification algorithm on ArcGIS software methods were used for wetting pattern dimensions determination. A Plexiglas soil column filled with homogeneous sandy textured soil profile was used in laboratory experiments. The non-contact thermal imaging technique was used to capture thermal and digital images at different water application times. The images were then classified using a maximum likelihood supervised classification algorithm on the ArcGIS software interface. The results revealed that cumulative water applied increased with an increase in application time. The maximum predicted depth and width for modified pipes were 12.4 and 18 cm, respectively. For the non-modified pipes, the dimension was 11.2 and 17 cm for depth and width, respectively. The maximum recorded wetted area was 46.56% under modified pipes compared with 41.01% for non-modified pipes. The higher uniform area coverage was achieved under modified clay pipes rather than in the clay porous pipes. The study concluded that the proposed imaging technique predicted the soil wetting pattern dimension with acceptable accuracy and provided for a simple and visual approach. Keywords: ArcGIS, Subsurface irrigation, Supervised classification system, Thermal imaging camera, Wetted depth, Wetted width.

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection.

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient μa and the reduced scattering coefficient μ’s were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears’ quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.

Real-time visualization of renal microperfusion using laser speckle contrast imaging.

.Significance: Intraoperative parameters of renal cortical microperfusion (RCM) have been associated with postoperative ischemia/reperfusion injury. Laser speckle contrast imaging (LSCI) could provide valuable information in this regard with the advantage over the current standard of care of being a non-contact and full-field imaging technique.Aim: Our study aims to validate the use of LSCI for the visualization of RCM on ex vivo perfused human-sized porcine kidneys in various models of hemodynamic changes.Approach: A comparison was made between three renal perfusion measures: LSCI, the total arterial renal blood flow (RBF), and sidestream dark-field (SDF) imaging in different settings of ischemia/reperfusion.Results: LSCI showed a good correlation with RBF for the reperfusion experiment (; ) and short- and long-lasting local ischemia (; and ; , respectively). The correlation decreased for low flow situations due to RBF redistribution. The correlation between LSCI and SDF (; ) showed superiority over RBF (; ).Conclusions: LSCI is capable of imaging RCM with high spatial and temporal resolutions. It can instantaneously detect local perfusion deficits, which is not possible with the current standard of care. Further development of LSCI in transplant surgery could help with clinical decision making.

Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.

An automated and multiparametric algorithm for objective analysis of meibography images

Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed. An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (rP ) and the false-negative rate (rN ) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94±0.02, the false-positive rate (rP )=6.02%±2.41%, and the false-negative rate (rN )=6.43%±1.98%; for meibomian gland segmentation, the similarity index (k)=0.87±0.01, the false-positive rate (rP )=4.35%±1.50%, and the-false negative rate (rN )=18.61%±1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.

Characterizing reduced scattering coefficient of normal human skin across different anatomic locations and Fitzpatrick skin types using spatial frequency domain imaging.

.Significance: Spatial frequency domain imaging (SFDI), a noncontact wide-field imaging technique using patterned illumination with multiple wavelengths, has been used to quantitatively measure structural and functional parameters of in vivo tissue. Using SFDI in a porcine model, we previously found that scattering changes in skin could potentially be used to noninvasively assess burn severity and monitor wound healing. Translating these findings to human subjects necessitates a better understanding of the variation in “baseline” human skin scattering properties across skin types and anatomical locations.Aim: Using SFDI, we aim to characterize the variation in the reduced scattering coefficient () for skin across a range of pigmentation and anatomic sites (including common burn locations) for normal human subjects. These measurements are expected to characterize baseline human skin properties to inform our use of SFDI for clinical burn severity and wound healing assessments.Approach: SFDI was used to measure in the visible- and near-infrared regime (471 to 851 nm) in 15 subjects at 10 anatomical locations. Subjects varied in age, gender, and Fitzpatrick skin type.Results: For all anatomical locations, the coefficient of variation in measured decreased with increasing wavelength. High intersubject variation in at visible wavelengths coincided with large values of the melanin extinction coefficient at those wavelengths. At 851 nm, where intersubject variation in was smallest for all anatomical locations and absorption from melanin is minimal, significant intrasubject differences in were observed at the different anatomical locations.Conclusions: Our study is the first report of wide-field mapping of human skin scattering properties across multiple skin types and anatomical locations using SFDI. Measured values varied notably between skin types at wavelengths where absorption from melanin was prominent. Additionally, varied considerably across different anatomical locations at 851 nm, where the confounding effects from melanin absorption are minimized.