Three-dimensional Optical Coherence Tomography Images Research Articles

Robust and accurate corneal interfaces segmentation in 2D and 3D OCT images.

Segmentation of corneal layer interfaces in optical coherence tomography (OCT) images is important for diagnostic and surgical purposes, while manual segmentation is a time-consuming and tedious process. This paper presents a novel technique for the automatic segmentation of corneal layer interfaces using customized initial layer estimation and a gradient-based segmentation method. The proposed method was also extended to three-dimensional OCT images. Validation was performed on two corneal datasets, one with 37 B-scan images of healthy human eyes and the other with a 3D volume scan of a porcine eye. The approach showed robustness in extracting different layer boundaries in the low-SNR region with lower computational cost but higher accuracy compared to existing techniques. It achieved segmentation errors below 2.1 pixels for both the anterior and posterior layer boundaries in terms of mean unsigned surface positioning error for the first dataset and 2.6 pixels (5.2 μm) for segmenting all three layers that can be resolved in the second dataset. On average, it takes 0.7 and 0.4 seconds to process a cross-sectional B-scan image for datasets one and two, respectively. Our comparative study also showed that it outperforms state-of-the-art methods for quantifying layer interfaces in terms of accuracy and time efficiency.

Success rate and influencing factors of a balloon-push technique: A new technique to remove side branch-jailed struts under three-dimensional optical coherence tomography guidance.

Kissing balloon inflation with distal guide wire recross can cause severe stent deformation depending on the stent link location with respect to the carina. The balloon-push technique, by which an inflated balloon is forced into the SB from the proximal main vessel (MV), is a feasible way to remove jailed struts without causing severe stent deformation. We investigated the procedural success rate, patterns of jailed strut removal at side branch (SB) orifices, factors related to failure of jailed strut removal, and follow-up angiogram results of the balloon-push technique. Between September 2015 and December 2020, 51 bifurcation stenting cases in which the balloon-push technique was used were enrolled. Based on three-dimensional optical coherence tomography images, strut removal with 1 stent crown length was defined as successful. Strut removal patterns were classified into two types: parallel-slide type (stent struts shifted distally into the MV lumen without inversion) and under-carina type (stent struts shifted distally under the carina with strut inversion or strut slide). Procedural success was attained in 39 cases (success rate: 76.5%). Parallel-slide type and under-carina type occurred in 43% and 33% of cases, respectively. Factors related to failure were trifurcation lesions and a smaller pushed balloon-SB artery ratio compared with those in success cases (0.95 ± 0.18 vs. 1.10 ± 0.22, p = 0.032). Follow-up angiography was performed in 37 cases, and 2 cases had binary in-stent restenosis. Removal of jailed struts with the balloon-push technique was feasible, without severe stent deformation, in bifurcation stentings.

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to . The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images.

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.

In Vivo Imaging of Newt Lens Regeneration: Novel Insights Into the Regeneration Process.

PurposeTo establish optical coherence tomography (OCT) as an in vivo imaging modality for investigating the process of newt lens regeneration.MethodsSpectral-domain OCT was employed for in vivo imaging of the newt lens regeneration process. A total of 37 newts were lentectomized and followed by OCT imaging over the course of 60 to 80 days. Histological images were obtained at several time points to compare with the corresponding OCT images. Volume measurements were also acquired.ResultsOCT can identify the key features observed in corresponding histological images based on the scattering differences from various eye tissues, such as the cornea, intact and regenerated lens, and the iris. Lens volume measurements from three-dimensional OCT images showed that the regenerating lens size increased linearly until 60 days post-lentectomy.ConclusionsUsing OCT imaging, we were able to track the entire process of newt lens regeneration in vivo for the first time. Three-dimensional OCT images allowed us to volumetrically quantify and visualize the dynamic spatial relationships between tissues during the regeneration process. Our results establish OCT as an in vivo imaging modality to track/analyze the entire lens regeneration process from the same animal.Translational RelevanceLens regeneration in newts represents a unique example of vertebrate tissue plasticity. Investigating the cellular and morphological events that govern this extraordinary process in vivo will advance our understanding and shed light on developing new therapies to treat blinding disorders in higher vertebrates.

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

PurposeTo compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpRNFL) thickness for the detection of glaucomatous progression.MethodsForty-four progressing glaucoma eyes (by stereophotograph assessment), 189 nonprogressing glaucoma eyes (by stereophotograph assessment), and 109 healthy eyes were followed for ≥3 years with ≥4 visits using OCT. The San Diego Automated Layer Segmentation Algorithm was used to automatically segment the RNFL layer from raw three-dimensional OCT images. For each longitudinal series, DL-AEs were used to generate individualized eye-based ROI maps by identifying RNFL regions of likely progression and no change. Sensitivities and specificities for detecting change over time and rates of change over time were compared for the DL-AE ROI and global cpRNFL thickness measurements derived from a 2.22-mm to 3.45-mm annulus centered on the optic disc.ResultsThe sensitivity for detecting change in progressing eyes was greater for DL-AE ROIs than for global cpRNFL annulus thicknesses (0.90 and 0.63, respectively). The specificity for detecting not likely progression in nonprogressing eyes was similar (0.92 and 0.93, respectively). The mean rates of change in DL-AE ROI were significantly faster than for cpRNFL annulus thickness in progressing eyes (−1.28 µm/y vs. −0.83 µm/y) and nonprogressing eyes (−1.03 µm/y vs. −0.78 µm/y).ConclusionsEye-specific ROIs identified using DL-AE analysis of OCT images show promise for improving assessment of glaucomatous progression.Translational RelevanceThe detection and monitoring of structural glaucomatous progression can be improved by considering eye-specific regions of likely progression identified using deep learning.

A miniaturized pre-calibration based Lissajous scanning fiber probe for endoscopic optical coherence tomography

In this paper we present a miniaturized pre-calibration based forward-viewing Lissajous scanning fiber probe for endoscopic optical coherence tomography (OCT). The probe is based on an asymmetric fiber cantilever driven by the piezoelectric bender to realize the two-dimensional (2D) Lissajous scanning, which can realize a relatively large scanning range under a low driving voltage. A capillary metal tube is mounted at the end of the main fiber to reduce the resonant frequency of the fiber cantilever. The relationship between the filling rate and the side-lobe number of the Lissajous scanning pattern is studied, and a method of selecting the orthogonal resonant frequency of the Lissajous scanning is proposed. Through the numerical simulation by COMSOL software, the structural parameters of the asymmetric fiber cantilever are determined. The orthogonal resonant frequencies of the asymmetric fiber cantilever are 169 Hz and 122 Hz. The lengths of the main imaging fiber, the auxiliary fiber and the metal capillary tube are 15.94 mm, 4.49 mm and 2 mm, respectively. The probe is fully packaged in a metal tube for endoscopic imaging. The focal spot and the working distance are 25 µm and 5 mm, respectively. The field of view is larger than 1.5 mm × 1.5 mm. The total rigid length and the outer diameter of the probe are 35 mm and 3.5 mm, respectively. The stability and repeatability of the Lissajous scanning trajectory, and the imaging stability with the rotation of the probe are investigated and verified. The probe is incorporated into a 50 kHz swept source OCT system. The axial resolution of the endoscopic OCT is 10.3 μm, and the imaging frame rate is 1 FPS (frames per second). The maximum signal-to-noise ratio of the imaging system is 110 dB. The imaging performance of the probe is validated by the 2D &lt;i&gt;en-face&lt;/i&gt; and three-dimensional volumetric OCT imaging of the high scattering sample and the biological tissue. The probe can be used for the endoscopic imaging of the human tooth. From the result we can distinguish the dental enamel, dental essence and the dental calculus. The developed forward-viewing Lissajous scanning fiber probe is expected to be used in dental applications such as early calculus detection.

Quantification of plant morphology and leaf thickness with optical coherence tomography

Optical coherence tomography (OCT) can be a valuable imaging tool for in vivo and label-free digital plant phenotyping. However, for imaging leaves, air-filled cavities limit the penetration depth and reduce the image quality. Moreover, up to now quantification of leaf morphology with OCT has been done in one-dimensional or two-dimensional images only, and has often been limited to relative measurements. In this paper, we demonstrate a significant increase in OCT imaging depth and image quality by infiltrating the leaf air spaces with water. In the obtained high-quality OCT images the top and bottom surface of the leaf are digitally segmented. Moreover, high-quality en face images of the leaf are obtained from numerically flattened leaves. Segmentation in three-dimensional OCT images is used to quantify the spatially resolved leaf thickness. Based on a segmented leaf image, the refractive index of an infiltrated leaf is measured to be 1.345±0.004, deviating only 1.2% from that of pure water. Using the refractive index and a correction for refraction effects at the air-leaf interface, we quantitatively mapped the leaf thickness. The results show that OCT is an efficient and promising technique for quantitative phenotyping on leaf and tissue level.

Two- and three-dimensional optical coherence tomography to differentiate degenerative changes in a rat meniscectomy model.

Optical coherence tomography (OCT) is an attractive tool for evaluating cartilage. We developed an OCT system that reconstructs and analyzes a three-dimensional (3D) OCT image by determining the cartilage surface and cartilage-bone boundary from the image taken with currently available OCT devices. We examined the usefulness of 3D renderings of OCT images. In a rat meniscectomized model, the tibia was harvested after 0, 2, 4, or 8 weeks (n = 6). We scanned 300 slices in the y-plane to cover a 4 × 3 × 6-mm section (x-plane; 10 µm × 400 pixels, y-plane; 10 µm × 300 pixels, z-plane; 12.66 µm × 500 pixels) of the medial tibial cartilage. The cartilage surface line and the cartilage-bone boundary were plotted semi-automatically. Slices from 300 two-dimensional (2D) sequential images were systematically and visually checked and corrected, as necessary. We set a region of interest in the cartilage and quantified the cartilage volume in the 3D image. The Osteoarthritis Research Society International (OARSI) histological score was also obtained. The cartilage volume determined using 3D OCT images was 0.291 ± 0.022 mm3 in the normal, 0.264 ± 0.009 mm3 at 2 weeks, 0.210 ± 0.012 mm3 at 4 weeks, and 0.205 ± 0.011 mm3 at 8 weeks. The cartilage volume significantly decreased at 4 and 8 weeks and was significantly correlated with the OARSI histological score (r = -0.674; P = .002). Although the 3D image information could be obtained from the 2D images, the 3D OCT images provided easier-to-understand information because the 3D reconstructed cartilage provided information about the smoothness of the surface, the area, and depth of the defect at a glance.

Refractive Changes After Corneal Stromal Filler Injection for the Correction of Hyperopia.

To evaluate a new non-ablative and adjustable procedure for laser ablative refractive corneal surgery in hyperopia using the injection of a biocompatible liquid filler material into a stromal pocket. A total of 120 stromal pockets were created using a clinical femtosecond laser system in 96 rabbit corneoscleral discs and 24 whole globes. Pockets were cut at a depth of 120 or 250 µm below the epithelial surface. Hyaluronic acid was injected manually into the pocket. To determine the refractive changes, three-dimensional optical coherence tomography images and a specifically developed picture recognition Matlab (The Mathworks) routine were used. After injection, a steepening of the anterior and flattening of the posterior corneal surface was observed, which led to hyperopic correction. The two main factors determining the amount of correction were the pocket depth and the injected volume. After the pocket was homogeneously filled, an initial refractive increase was observed, followed by a linear relation between the injected volume and the refraction increase. This possible clinical protocol for controlled refraction correction of hyperopia suggests a potential readjustable clinical application. [J Refract Surg. 2020;36(6):406-414.].

A feasibility study of OCT for anatomical and vascular phenotyping of mouse embryo

The embryo phenotyping of genetic murine model is invaluable when investigating functions of genes underlying embryonic development and birth defect. Although traditional imaging technologies such as ultrasound are very useful for evaluating phenotype of murine embryos, the use of advanced techniques for phenotyping is desirable to obtain more information from genetic research. This letter tests the feasibility of optical coherence tomography (OCT) as a high-throughput phenotyping tool for murine embryos. Three-dimensional OCT imaging is performed for live and cleared mouse embryos in the late developmental stage (embryonic day 17.5). By using a dynamic focusing method and OCT angiography (OCTA) approach, our OCT imaging of the embryo exhibits rapid and clean visualization of organ structures deeper than 5 mm and complex microvasculature of perfused blood vessels in the murine embryonic body. This demonstration suggests that OCT imaging can be useful for comprehensively assessing embryo anatomy and angiography of genetically engineered mice.

Steerable OCT catheter for real-time assistance during teleoperated endoscopic treatment of colorectal cancer.

When detected early, colorectal cancer can be treated with minimally invasive flexible endoscopy. However, since only specialized experts can delineate margins and perform endoscopic resections of lesions, patients still often undergo colon resections. To better assist in the performance of surgical tasks, a robotized flexible interventional endoscope was previously developed, having two additional side channels for surgical instrument. We propose to enhance the imaging capabilities of this device by combining it with optical coherence tomography (OCT). For this purpose, we have developed a new steerable OCT instrument with an outer diameter of 3.5 mm. The steerable instrument is terminated with a 2 cm long transparent sheath to allow three-dimensional OCT imaging using a side-focusing optical probe with two external scanning actuators. The instrument is connected to an OCT imaging system built around the OCT Axsun engine, with a 1310 nm center wavelength swept source laser and 100 kHz A-line rate. Once inserted in one of the side channels of the robotized endoscope, bending, rotation and translation of the steerable OCT instrument can be controlled by a physician using a joystick. Ex vivo and in vivo tests show that the novel, steerable and teleoperated OCT device enhances dexterity, allowing for inspection of the surgical field without the need for changing the position of the main endoscope.

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa.

Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two-dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three-dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real-time analytic tool in the assessment of surgical margins.

Annotated data analysis of three-dimensional optical coherence tomography of the retina for the creation of an intelligent database

Optical coherence tomography is one of the key diagnostic methods used in ophthalmology and has a high potential for application in an automatic analysis. In this study, we collected, annotated, and analyzed 44 three-dimensional optical coherence tomography images obtained in 39 patients suffering from central serous chorioretinopathy. A semantic annotation of the pathological changes includes three classes: (1) retinal neuroepithelial detachment, (2) retinal pigment epithelium alteration, and (3) a leakage zone. Machine learning methods have been applied to distinguish classes 2 and 3 based on the brightness characteristics of optical coherent tomography images. Intra-group clustering of the class instances showed that the separation of two groups of changes in class 2 can be associated with differences in the volumetric characteristics, whereas the brightness characteristics in class 3 differ significantly depending on the age of the patients, which can be used to predict the course of the disease.

A deep separable neural network for human tissue identification in three-dimensional optical coherence tomography images

This research proposes a dilated depthwise separable network for human tissue identification using three-dimensional (3D) optical coherence tomography (OCT) images. Automatic human tissue identification has made it possible for fast pathological tissue analyses, detecting tissue changes over time, and efficiently making a precise therapy treatment plan. 3D medical image classification is a challenging task because of the indistinct tissue characteristics and computational efficiency. To address the challenge, a deep dilated depthwise separable convolutional neural network is proposed in this research. A depthwise separable architecture is introduced to improve parameter utilization efficiency. Dilated convolutions are applied to systematically aggregate multiscale contextual information and provide a large receptive field with a small number of trainable weights, which provide computational benefit. 2D convolutions are applied in the proposed model to enhance the computational efficiency. The constructed model is tested by performing a multi-class human thyroid tissue classification on 3D OCT images. For comparison, experimental results are obtained for texture feature-based shallow learning models and typical deep learning classification models. The results show that the proposed DDSCN model outperforms those state-of-art models and can improve accuracy by 3.2% compared with the best texture-based model and 2.27% compared with the best CNN model. The proposed deep model provides significant potential for the applicability of the deep learning technique to analyze medical images of human tissue while advancing the next generation of OCT-based real-time surgery image guidance.

Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks

In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, the optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of the proposed multi-input architecture are learned by considering the consistency between the overall output and the contribution of each input image. The proposed MIFCN method is compared with the state-of-the-art denoising methods adopted on OCT images of normal and age-related macular degeneration eyes in a quantitative and qualitative manner.

Quantitative Evaluation of Skin Surface Roughness Using Optical Coherence Tomography &lt;italic&gt;In Vivo &lt;/italic&gt;

The quantitative monitoring of skin topography is important in the field of cosmetics and dermatology. The most widespread method for determining skin roughness in vivo is to use skin microrelief, PRIMOS device, which allows a noninvasive, fast, and direct measurement of the skin surface. However, it has drawbacks, such as the interference of backscattering from volumetric skin and motion artifacts. In this study, we demonstrate the potential of optical coherence tomography (OCT) for providing reliable and quantitative skin surface roughness. In order to evaluate the performance of OCT for skin surface analysis, different types of skin phantoms are fabricated and measured. We utilize OCT to identify the effect of cosmetics as well as human skin topology for various aging groups and different skin regions. Skin surface roughness parameters based on ISO 25178 part 2 standard definitions are then derived from home-built image processing software and compared with one acquired from PRIMOS. Our results show that skin surface geometry acquired from three-dimensional OCT images is well quantified to complex wrinkle structure and robust to the angle of the subject. Since OCT enables to present quantitative skin topology and volumetric skin anatomy simultaneously, it would be a useful tool to deliver comprehensive and intuitive information in dynamic skin observations.

Clinical and surgical implications of intraoperative optical coherence tomography imaging for benign pediatric vocal fold lesions

IntroductionBenign vocal disorders in children include an extensive list of abnormalities creating a variety of debilitating levels of dysphonia. Precise delineation of the benign lesion type and margins may have significant public health implications in children. An innovative technology such as optical coherence tomography (OCT) is being explored to delineate pediatric benign laryngeal lesions. An accurate assessment of the subepithelial morphology may help towards tailoring more personalized therapeutic treatments. This study was established to highlight key morphological and optical features of benign pediatric laryngeal lesions using intraoperative OCT and to suggest clinical implications that arise from such optical imaging. Methods and materialsThis in vivo study was performed at Massachusetts Eye and Ear Infirmary. Intraoperative imaging was performed on twenty-five pediatric patients ranging from 1 year to 16 years of age presenting hoarseness. Three-dimensional OCT images of benign laryngeal lesions or a subsite of the lesion were acquired. ResultsHigh-resolution OCT images of 25 patients with benign laryngeal lesions such as nodules, cysts, Reinke's edema, vocalis sulcus, and papilloma revealed distinct and specific morphological differences with normal tissue. Nodules show a symmetrical superficial remodeling of the vocal fold epithelial layer and the basement membrane. Cysts have oval-like shape and are either superficial or deeply located in the lamina propria. Sulcus vocalis OCT imaging allows characterizing if the lesion is shallow or deep according to Ford's classification system. Reinke's edema of the mucosa can be observed and quantified, which may lead to suspicion on the underlying social and medical conditions. Finally, the ability to assess margins and depth of invasion of papilloma lesions is demonstrated, raising the possibility to use OCT with angiolytic lasers for patient-tailored treatments. ConclusionsOCT imaging of benign pediatric vocal lesions is promising as it could improve preoperative decision-making and possibly peroperative imaging-guidance for patient-tailored treatments. An assessment of the optical contrast between healthy and abnormal tissue may help towards a more qualitative and quantitative approach to current standard care, especially when diagnosis remains unclear.

Utility of Optical Coherence Tomography for Guiding Laser Therapy Among Patients With Recurrent Respiratory Papillomatosis

Recurrent respiratory papillomatosis (RRP) is a viral-induced disease caused by human papillomavirus and the second leading cause of dysphonia in children; however, neither a cure nor a definitive surgical treatment is currently available for RRP. Although laser therapy is often used in the treatment of RRP, the lack of real-time laser-tissue interaction feedback undermines the ability of physicians to provide treatments with low morbidity. Therefore, an intraoperative tool to monitor and control laser treatment depth is needed. To investigate the potential of combining optical coherence tomography (OCT) with laser therapy for patient-tailored laryngeal RRP treatments. This in vivo study was performed at the Massachusetts Eye and Ear Infirmary from February 1, 2017, to September 1, 2017. Three-dimensional OCT images were acquired before, during, and after photoangiolytic laser therapy in 10 pediatric patients with a history of papilloma growth who presented with lesions and hoarseness. Whether intraoperative OCT monitoring of changes in optical scattering and absorption provides quantitative information to control thermal damage in tissue. Among the 10 pediatric patients (age range, 4-11 years; 6 male) included in the study, high-resolution OCT images revealed epithelial hyperplasia with clear RRP lesion margins. Images acquired during therapy indicated coagulation deep in tissue, and posttherapy images showed the ability to quantify the amount of tissue ablated by the photoangiolytic laser. Concurrent use of OCT imaging and laser therapy may improve postoperative outcomes for patients with RRP by delivering an optimal, patient-tailored treatment. Additional studies investigating the correlation between optical properties with vocal outcomes are required.

Extraction of the Polyurethane Layer in Textile Composites for Textronics Applications Using Optical Coherence Tomography.

This article presents a new method for the extraction and measurement of the polyurethane layer of Cordura textile composites using optical coherence tomography. The knowledge of coating layer properties in these composites is very important, as it affects mechanical parameters such as stiffness and bending rigidity. Unlike microscopic measurements, which require cross-section samples of the material, the proposed approach is non-invasive. The method is based on detecting the top and bottom boundaries of the polyurethane layer in Optical Coherence Tomography (OCT) images using image processing methods, namely edge enhancement filtering, thresholding and spline smoothing. The cover layer measurement results obtained from a three-dimensional OCT image of the composite fabric are presented as the thickness maps. The average values of the layer thicknesses measured with the OCT method for four types of Cordura showed a high correlation with the results obtained from microscopic measurements (Pearson correlation coefficient ), which confirms the accuracy of the OCT method.