Texture analysis of speckle in optical coherence tomography images of tissue phantoms

Exploring Indicators of Subcutaneous Tissue Fluid Accumulation in Breast Cancer-Related Lymphedema Patients Using Fractal Analysis with Virtual Volume

Background: Breast cancer treatment sometimes causes a chronic swelling of the arm called breast cancer-related lymphedema (BCRL). Its progression is believed to be irreversible and is accompanied by tissue fibrosis and lipidosis, so preventing lymphedema from progressing by appropriate intervention at the site of fluid accumulation at an early stage is crucial. The tissue structure can be evaluated in real time by ultrasonography, and this study aims at assessing the ability of fractal analysis using virtual volume in detecting fluid accumulation within BCRL subcutaneous tissue via ultrasound imaging. Methods and Results: We worked with 21 women who developed BCRL (International Society of Lymphology stage II) after unilateral breast cancer treatment. Their subcutaneous tissues were scanned with an ultrasound system (Sonosite Edge II; Sonosite, Inc., FUJIFILM) using a 6- to 15-MHz linear transducer. Then, a 3-Tesla MR system was used to confirm fluid accumulation in the corresponding area of the ultrasound system. Significant differences in both H + 2 and complexity were observed among the three groups (with hyperintense area, without hyperintense area, and unaffected side) (p < 0.05). Post hoc analysis (Mann-Whitney U test; Bonferroni correction p < 0.0167) revealed a significant difference for "complexity." The evaluation of the distribution in Euclidean space showed that the variation of the distribution decreased in the order of unaffected, without hyperintense area, and with hyperintense area. Conclusion: The "complexity" of the fractal using virtual volume seems to be an effective indicator of the presence or absence of subcutaneous tissue fluid accumulation in BCRL.

Theoretical model for en face optical coherence tomography imaging and its application to volumetric differential contrast imaging

A new formulation of the lateral imaging process of point-scanning optical coherence tomography (OCT) and a new differential contrast method designed by using this formulation are presented. The formulation is based on a mathematical sample model called the dispersed scatterer model (DSM), in which the sample is represented as a material with a spatially slowly varying refractive index and randomly distributed scatterers embedded in the material. It is shown that the formulation represents a meaningful OCT image and speckle as two independent mathematical quantities. The new differential contrast method is based on complex signal processing of OCT images, and the physical and numerical imaging processes of this method are jointly formulated using the same theoretical strategy as in the case of OCT. The formula shows that the method provides a spatially differential image of the sample structure. This differential imaging method is validated by measuring in vivo and in vitro samples.

Signal-carrying speckle in optical coherence tomography: a methodological review on biomedical applications

SIGNIFICANCE

Speckle has historically been considered a source of noise in coherent light imaging. However, a number of works in optical coherence tomography (OCT) imaging have shown that speckle patterns may contain relevant information regarding subresolution and structural properties of the tissues from which it is originated.

AIM

The objective of this work is to provide a comprehensive overview of the methods developed for retrieving speckle information in biomedical OCT applications.

APPROACH

PubMed and Scopus databases were used to perform a systematic review on studies published until December 9, 2021. From 146 screened studies, 40 were eligible for this review.

RESULTS

The studies were clustered according to the nature of their analysis, namely static or dynamic, and all features were described and analyzed. The results show that features retrieved from speckle can be used successfully in different applications, such as classification and segmentation. However, the results also show that speckle analysis is highly application-dependant, and the best approach varies between applications.

CONCLUSIONS

Several of the reviewed analyses were only performed in a theoretical context or using phantoms, showing that signal-carrying speckle analysis in OCT imaging is still in its early stage, and further work is needed to validate its applicability and reproducibility in a clinical context.

Textural Feature Analysis of Optical Coherence Tomography Phantoms

Optical coherence tomography (OCT) is an imaging technique based on interferometry of backscattered lights from materials and biological samples. For the quantitative evaluation of an OCT system, artificial optical samples or phantoms are commonly used. They mimic the structure of biological tissues and can provide a quality standard for comparison within and across devices. Phantoms contain medium matrix and scattering particles within the dimension range of target biological structures such as the retina. The aim was to determine if changes in speckle derived optical texture could be employed to classify the OCT phantoms based on their structural composition. Four groups of phantom types were prepared and imaged. These comprise different concentrations of a medium matrix (gelatin solution), different sized polystyrene beads (PBs), the volume of PBs and different refractive indices of scatterers (PBs and SiO2). Texture analysis was applied to detect subtle optical differences in OCT image intensity, surface coarseness and brightness of regions of interest. A semi-automated classifier based on principal component analysis (PCA) and support vector machine (SVM) was applied to discriminate the various texture models. The classifier detected correctly different phantom textures from 82% to 100%, demonstrating that analysis of the texture of OCT images can be potentially used to discriminate biological structure based on subtle changes in light scattering.

Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

Purpose

A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images.

Methods

Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis.

Results

We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%.

Conclusions

An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences.

Prediction of the Presence of Fluid Accumulation in the Subcutaneous Tissue in BCRL Using Texture Analysis of Ultrasound Images

Background: Breast cancer-related lymphedema (BCRL) is a chronic swelling of the arm due to breast cancer treatment. Lymphedema is diagnosed and staged on the basis of limb circumference measurements and the patient's subjective symptoms, which have poor reproducibility and objectivity: these cannot detect any fluid accumulation in the tissue. Ultrasonography is a feasible noninvasive technique that can be used to evaluate tissue structure in real time. This study aimed to assess the ability of texture features for discriminating the presence of accumulated fluid within the subcutaneous tissue of BCRL using ultrasound (US) imaging. Methods and Results: This study included 20 women who were treated for unilateral breast cancer and who subsequently developed BCRL (International Society of Lymphology stage II). Subcutaneous tissue was scanned through an US system (Sonosite Edge II; Sonosite, Inc., FUJIFILM) using a 6- to 15-MHz linear transducer to assess the ability of texture features for discriminating the presence of accumulated fluid within the subcutaneous tissue of BCRL. Fluid accumulation was observed using a 3-Tesla MR system under double-echo steady-state conditions. There was a significant difference among the three groups (with hyperintense area, without hyperintense area, and unaffected side) in 11 of 14 textural features (p < 0.05). Post hoc analysis (Mann-Whitney U test; Bonferroni correction p < 0.0167) revealed significant differences in seven textural features within the hyperintense area. Conclusions: This study revealed that seven texture features quantified by US imaging data can provide information regarding fluid accumulation in the subcutaneous tissue of lymphedema.

High-resolution in vivo imaging of peripheral nerves using optical coherence tomography: a feasibility study

OBJECTIVE

Because of their complex topography, long courses, and small diameters, peripheral nerves are challenging structures for radiological diagnostics. However, imaging techniques in the area of peripheral nerve diseases have undergone unexpected development in recent decades. They include MRI and high-resolution sonography (HRS). Yet none of those imaging techniques reaches a resolution comparable to that of histological sections. Fascicles are the smallest discernable structure. Optical coherence tomography (OCT) is the first imaging technique that is able to depict a nerve's ultrastructure at micrometer resolution. In the current study, the authors present an in vivo assessment of human peripheral nerves using OCT.

METHODS

OCT measurement was performed in 34 patients with different peripheral nerve pathologies, i.e., nerve compression syndromes. The nerves were examined during surgery after their exposure. Only the sural nerve was twice examined ex vivo. The Thorlabs OCT systems Callisto and Ganymede were used. For intraoperative use, a hand probe was covered with a sterile foil. Different postprocessing imaging techniques were applied and evaluated. In order to highlight certain structures, five texture parameters based on gray-level co-occurrence matrices were calculated according to Haralick.

RESULTS

The intraoperative use of OCT is easy and intuitive. Image artifacts are mainly caused by motion and the sterile foil. If the artifacts are kept at a low level, the hyporeflecting bundles of nerve fascicles and their inner parts can be displayed. In the Haralick evaluation, the second angular moment is most suitable to depict the connective tissue.

CONCLUSIONS

OCT is a new imaging technique that has shown promise in peripheral nerve surgery for particular questions. Its resolution exceeds that provided by recent radiological possibilities such as MRI and HRS. Since its field of view is relatively small, faster acquisition times would be highly desirable and have already been demonstrated by other groups. Currently, the method resembles an optical biopsy and can be a supplement to intraoperative sonography, giving high-resolution insight into a suspect area that has been located by sonography in advance.

En-face optical coherence tomography for the detection of cancer in prostatectomy specimens: Quantitative analysis in 20 patients

The increase histopathological evaluation of prostatectomy specimens rises the workload on pathologists. Automated histopathology systems, preferably directly on unstained specimens, would accelerate the pathology workflow. In this study, we investigate the potential of quantitative analysis of optical coherence tomography (OCT) to separate benign from malignant prostate tissue automatically. Twenty fixated prostates were cut, from which 54 slices were scanned by OCT. Quantitative OCT metrics (attenuation coefficient, residue, goodness-of-fit) were compared for different tissue types, annotated on the histology slides. To avoid misclassification, the poor-quality slides, and edges of annotations were excluded. Accurate registration of OCT data with histology was achieved in 31 slices. After removing outliers, 56% of the OCT data was compared with histopathology. The quantitative data could not separate malignant from benign tissue. Logistic regression resulted in malignant detection with a sensitivity of 0.80 and a specificity of 0.34. Quantitative OCT analysis should be improved before clinical use.

Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue

Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Optical coherence tomography (OCT) has been applied successfully to experimentally image the ovaries in vivo; however, a robust method for analysis is still required to provide quantitative diagnostic information. Recently, texture analysis has proved to be a useful tool for tissue characterization; unfortunately, existing work in the scope of OCT ovarian imaging is limited to only analyzing 2D sub-regions of the image data, discarding information encoded in the full image area, as well as in the depth dimension. Here we address these challenges by testing three implementations of texture analysis for the ability to classify tissue type. First, we test the traditional case of extracted 2D regions of interest; then we extend this to include the entire image area by segmenting the organ from the background. Finally, we conduct a full volumetric analysis of the image volume using 3D segmented data. For each case, we compute features based on the Grey-Level Co-occurence Matrix and also by introducing a new approach that evaluates the frequency distribution in the image by computing the energy density. We test these methods on a mouse model of ovarian cancer to differentiate between age, genotype, and treatment. The results show that the 3D application of texture analysis is most effective for differentiating tissue types, yielding an average classification accuracy of 78.6%. This is followed by the analysis in 2D with the segmented image volume, yielding an average accuracy of 71.5%. Both of these improve on the traditional approach of extracting square regions of interest, which yield an average classification accuracy of 67.7%. Thus, applying texture analysis in 3D with a fully segmented image volume is the most robust approach to quantitatively characterizing ovarian tissue.

Potentials and pitfalls of gold-silica nanoshell as the exogenous contrast agent for optical diagnosis of cancers: a numerical parametric study

For nanoshell-assisted optical detection of cancers, gold shell, silica core (gold-silica) nanoshells are engineered to be the exogenous contrast agent. This work has performed systematic numerical parametric study to investigate the nonlinear dependences of the hemisphere diffuse reflectance on gold-silica nanoshells, laser irradiance, and hosting biology tissue. Planar phantom based tissue models have been constructed as platforms for study. The radiant transport equation (RTE) has been applied to mathematically describe the interactions among laser lights, hosting tissues, and hosted nanoshells. The diffuse reflectance signal under various combinations of parametric conditions has been computed and analyzed. Parametric parameters whose effects on the diffuse reflectance signal have been investigated are: (1) optical properties of a nanoshell generic, (2) nanoshell volume fraction, which is an indicator of nanoshell accumulation in the target tissue site, (3) the width of irradiating laser beam, and (4) thickness of the tissue slab. Seven nanoshell generics have been tested as the exogenous contrast agent including the R[50, 10] (radius of silica core is 50 nm and thickness of gold shell is 10 nm), R[55, 25], R[40, 15], R[40, 40], R[104, 23], R[75, 40] and R[154, 24] nanoshells. It has been found the R[55, 25] nanoshell works best as the exogenous contrast agent, the R[75, 40] and R[104, 23] nanoshells show good potentials as well while the R[50, 10] and R[40, 15] nanoshells should be avoided for diagnostic usage. The practice of neglecting the absorption characteristic of the exogenous contrast agent, which is quite common among the bio-nano community, has been proven to end up with an over-prediction of the effectiveness of the exogenous contrast agent. Such practice therefore is not well justified and should be avoided in future research. Interactions among laser lights, the tissue and nanoshells are highly nonlinear, demonstrated by that nanoshell generics with totally different optical properties might have similar effects on the diffuse reflectance signal and vice versa. Prior to any bench experiment, preliminary numerical investigation as this work has showcased is highly recommended.

Wavelet analysis on time-frequency plane of optical coherence tomography: simultaneous signal quality improvement in structural and velocity images

In this Letter, we utilize one-dimensional wavelet analysis to improve the quality of morphology images and velocity profiles of optical coherence tomography simultaneously, by performing analysis on the complex time-frequency plane of raw interferograms, prior to image construction. The results indicate a robust signal improvement that also preserves accuracy for both morphology and velocity information and has been demonstrated on a variety of samples with diverse flow speeds and morphologies.

Learnable despeckling framework for optical coherence tomography images

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

Monitoring kidney microanatomy changes during ischemia-reperfusion process using texture analysis of OCT images

kidney ischemia-reperfusion (I/R) accounts for the majority of acute kidney injury cases, whose consequences are commonly encountered after kidney transplantation. Optical coherence tomography (OCT) has been applied to image changes in kidney microanatomy and microcirculation. In this paper, we demonstrate a quantitative method for monitoring kidney status during ischemia-reperfusion process using texture properties of OCT images. This approach employs skewness to measure the distribution of en face OCT image intensities at different depths, thus allowing differentiating ischemia-reperfusion status of kidney. The skewness analysis based on quantitative intensity shows promise for monitoring kidney status during ischemia-reperfusion, and the potential for evaluating the viability of transplant kidney.

Quantitative characterization of mechanically indented in vivo human skin in adults and infants using optical coherence tomography

Influenced by both the intrinsic viscoelasticity of the tissue constituents and the time-evolved redistribution of fluid within the tissue, the biomechanical response of skin can reflect not only localized pathology but also systemic physiology of an individual. While clinical diagnosis of skin pathologies typically relies on visual inspection and manual palpation, a more objective and quantitative approach for tissue characterization is highly desirable. Optical coherence tomography (OCT) is an interferometry-based imaging modality that enables in vivo assessment of cross-sectional tissue morphology with micron-scale resolution, which surpasses those of most standard clinical imaging tools, such as ultrasound imaging and magnetic resonance imaging. This pilot study investigates the feasibility of characterizing the biomechanical response of in vivo human skin using OCT. OCT-based quantitative metrics were developed and demonstrated on the human subject data, where a significant difference between deformed and nondeformed skin was revealed. Additionally, the quantified postindentation recovery results revealed differences between aged (adult) and young (infant) skin. These suggest that OCT has the potential to quantitatively assess the mechanically perturbed skin as well as distinguish different physiological conditions of the skin, such as changes with age or disease.

Classification and analysis of human ovarian tissue using full field optical coherence tomography

In this study, a full field optical coherence tomography (FFOCT) system was used to analyze and classify normal and malignant human ovarian tissue. 14 ovarian tissue samples (7 normal, 7 malignant) were imaged with the FFOCT system and five features were extracted by analyzing the normalized image histogram from 56 FFOCT images, based on the differences in the morphology of the normal and malignant tissue samples. A generalized linear model (GLM) classifier was trained using 36 images, and sensitivity of 95.3% and specificity of 91.1% was obtained. 20 images were used to test the model, and a sensitivity of 91.6% and specificity of 87.7% was obtained.

Diagnosis of subglottic stenosis in a rabbit model using long-range optical coherence tomography

OBJECTIVES/HYPOTHESIS

Current imaging modalities lack the necessary resolution to diagnose subglottic stenosis. The aim of this study was to use optical coherence tomography (OCT) to evaluate nascent subglottic mucosal injury and characterize mucosal thickness and structural changes using texture analysis in a simulated intubation rabbit model.

STUDY DESIGN

Prospective animal study in rabbits.

METHODS

Three-centimeter-long sections of endotracheal tubes (ETT) were endoscopically placed in the subglottis and proximal trachea of New Zealand White rabbits (n = 10) and secured via suture. OCT imaging and conventional endoscopic video was performed just prior to ETT segment placement (day 0), immediately after tube removal (day 7), and 1 week later (day 14). OCT images were analyzed for airway wall thickness and textural properties.

RESULTS

Endoscopy and histology of intubated rabbits showed a range of normal to edematous tissue, which correlated with OCT images. The mean airway mucosal wall thickness measured using OCT was 336.4 μm (day 0), 391.3 μm (day 7), and 420.4 μm (day 14), with significant differences between day 0 and day 14 (P = .002). Significance was found for correlation and homogeneity texture features across all time points (P < .05).

CONCLUSIONS

OCT is a minimally invasive endoscopic imaging modality capable of monitoring progression of subglottic mucosal injury. This study is the first to evaluate mucosal injury during simulated intubation using serial OCT imaging and texture analysis. OCT and texture analysis have the potential for early detection of subglottic mucosal injury, which could lead to better management of the neonatal airway and limit the progression to stenosis.

LEVEL OF EVIDENCE

NA Laryngoscope, 127:64-69, 2017.

Robust motion tracking based on adaptive speckle decorrelation analysis of OCT signal

Speckle decorrelation analysis of optical coherence tomography (OCT) signal has been used in motion tracking. In our previous study, we demonstrated that cross-correlation coefficient (XCC) between Ascans had an explicit functional dependency on the magnitude of lateral displacement (δx). In this study, we evaluated the sensitivity of speckle motion tracking using the derivative of function XCC(δx) on variable δx. We demonstrated the magnitude of the derivative can be maximized. In other words, the sensitivity of OCT speckle tracking can be optimized by using signals with appropriate amount of decorrelation for XCC calculation. Based on this finding, we developed an adaptive speckle decorrelation analysis strategy to achieve motion tracking with optimized sensitivity. Briefly, we used subsequently acquired Ascans and Ascans obtained with larger time intervals to obtain multiple values of XCC and chose the XCC value that maximized motion tracking sensitivity for displacement calculation. Instantaneous motion speed can be calculated by dividing the obtained displacement with time interval between Ascans involved in XCC calculation. We implemented the above-described algorithm in real-time using graphic processing unit (GPU) and demonstrated its effectiveness in reconstructing distortion-free OCT images using data obtained from a manually scanned OCT probe. The adaptive speckle tracking method was validated in manually scanned OCT imaging, on phantom as well as in vivo skin tissue.

Spatiotemporal correlation of optical coherence tomography in-vivo images of rabbit airway for the diagnosis of edema

Detection of an early stage of subglottic edema is vital for airway management and prevention of stenosis, a life-threatening condition in critically ill neonates. As an observer for the task of diagnosing edema in vivo, we investigated spatiotemporal correlation (STC) of full-range optical coherence tomography (OCT) images acquired in the rabbit airway with experimentally simulated edema. Operating the STC observer on OCT images generates STC coefficients as test statistics for the statistical decision task. Resulting from this, the receiver operating characteristic (ROC) curves for the diagnosis of airway edema with full-range OCT in-vivo images were extracted and areas under ROC curves were calculated. These statistically quantified results demonstrated the potential clinical feasibility of the STC method as a means to identify early airway edema.

Non-invasive detection of early retinal neuronal degeneration by ultrahigh resolution optical coherence tomography

Optical coherence tomography (OCT) has revolutionises the diagnosis of retinal disease based on the detection of microscopic rather than subcellular changes in retinal anatomy. However, currently the technique is limited to the detection of microscopic rather than subcellular changes in retinal anatomy. However, coherence based imaging is extremely sensitive to both changes in optical contrast and cellular events at the micrometer scale, and can generate subtle changes in the spectral content of the OCT image. Here we test the hypothesis that OCT image speckle (image texture) contains information regarding otherwise unresolvable features such as organelle changes arising in the early stages of neuronal degeneration. Using ultrahigh resolution (UHR) OCT imaging at 800 nm (spectral width 140 nm) we developed a robust method of OCT image analyses, based on spatial wavelet and texture-based parameterisation of the image speckle pattern. For the first time we show that this approach allows the non-invasive detection and quantification of early apoptotic changes in neurons within 30 min of neuronal trauma sufficient to result in apoptosis. We show a positive correlation between immunofluorescent labelling of mitochondria (a potential source of changes in cellular optical contrast) with changes in the texture of the OCT images of cultured neurons. Moreover, similar changes in optical contrast were also seen in the retinal ganglion cell- inner plexiform layer in retinal explants following optic nerve transection. The optical clarity of the explants was maintained throughout in the absence of histologically detectable change. Our data suggest that UHR OCT can be used for the non-invasive quantitative assessment of neuronal health, with a particular application to the assessment of early retinal disease.

Automatic analysis of selected choroidal diseases in OCT images of the eye fundus

Introduction

This paper describes a method for automatic analysis of the choroid in OCT images of the eye fundus in ophthalmology. The problem of vascular lesions occurs e.g. in a large population of patients having diabetes or macular degeneration. Their correct diagnosis and quantitative assessment of the treatment progress are a critical part of the eye fundus diagnosis.

Material and method

The study analysed about 1’000 OCT images acquired using SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The proposed algorithm for image analysis enabled to analyse the texture of the choroid portion located beneath the RPE (Retinal Pigment Epithelium) layer. The analysis was performed using the profiled algorithm based on morphological analysis and texture analysis and a classifier in the form of decision trees.

Results

The location of the centres of gravity of individual objects present in the image beneath the RPE layer proved to be important in the evaluation of different types of images. In addition, the value of the standard deviation and the number of objects in a scene were equally important. These features enabled classification of three different forms of the choroid that were related to retinal pathology: diabetic edema (the classification gave accuracy ACC₁ = 0.73), ischemia of the inner retinal layers (ACC₂ = 0.83) and scarring fibro vascular tissue (ACC₃ = 0.69). For the cut decision tree the results were as follows: ACC₁ = 0.76, ACC₂ = 0.81, ACC₃ = 0.68.

Conclusions

The created decision tree enabled to obtain satisfactory results of the classification of three types of choroidal imaging. In addition, it was shown that for the assumed characteristics and the developed classifier, the location of B-scan does not significantly affect the results. The image analysis method for texture analysis presented in the paper confirmed its usefulness in choroid imaging. Currently the application is further studied in the Clinical Department of Ophthalmology in the District Railway Hospital in Katowice, Medical University of Silesia, Poland.

Evaluation of zebrafish brain development using optical coherence tomography

The zebrafish is a well-established model system used to study and understand various human biological processes. The present study used OCT to investigate growth of the adult zebrafish brain. Twenty zebrafish were studied, using their standard lengths as indicators of their age. Zebrafish brain aging was evaluated by analyzing signal attenuation rates and texture features in regions of interest (ROIs). Optical scattering originates from light interaction with biological structures. During development, the zebrafish brain gains cells. Signal attenuation rate, therefore, increases with increasing zebrafish brain age. This study's analyses of texture features could not identify aging in zebrafish brain. These results, therefore, indicated that the OCT signal attenuation rate can indicate zebrafish brain aging, and its analysis provides a more effective means of observing zebrafish brain aging than texture features analysis. Using OCT system could further increase the technique's potential for recognition and monitoring of zebrafish brain development.

Robust spectral-domain optical coherence tomography speckle model and its cross-correlation coefficient analysis

In this study, we propose a generic speckle simulation for optical coherence tomography (OCT) signal, by convolving the point-spread function (PSF) of the OCT system with the numerically synthesized random sample field. We validate our model and use the simulation method to study the statistical properties of cross-correlation coefficients between A-scans, which have been recently applied in transverse motion analysis by our group. The results of simulation show that oversampling is essential for accurate motion tracking; exponential decay of OCT signal leads to an underestimate of motion that can be corrected; lateral heterogeneity of sample leads to an overestimate of motion for a few pixels corresponding to the structural boundary.

Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.

Speckle properties of the logarithmically transformed signal in optical coherence tomography

We discuss the statistical properties of speckle of the logarithmically transformed signal in optical coherence tomography (OCT) both theoretically and experimentally. OCT signals of Intralipid solution with different volume particle concentrations ρ (correspondingly, scattering coefficient μ(s) ranges from 1.25 to 25.11 mm(-1)) were measured and analyzed under two different focusing conditions [numerical apertures (NAs) of the objective lens of 0.13 and 0.25]. We found that the effect of the speckle noise can be suppressed by displaying OCT images in the logarithmic scale and by using the objective lens with a higher NA. We also found that the speckle properties are correlated with the scattering properties of the sample, which may be used to characterize the scattering properties of biological tissue. The simulated OCT image and the in vitro OCT image of a rat liver are used as examples to demonstrate the feasibility of the method.

Diagnostic efficacy of computer extracted image features in optical coherence tomography of the precancerous cervix

PURPOSE

To determine the diagnostic efficacy of optical coherence tomography (OCT) to identify cervical intraepithelial neoplasia (CIN) grade 2 or higher by computer-aided diagnosis (CADx).

METHODS

OCT has been investigated as a screening/diagnostic tool in the management of preinvasive and early invasive cancers of the uterine cervix. In this study, an automated algorithm was developed to extract OCT image features and identify CIN 2 or higher. First, the cervical epithelium was detected by a combined watershed and active contour method. Second, four features were calculated: The thickness of the epithelium and its standard deviation and the contrast between the epithelium and the stroma and its standard deviation. Finally, linear discriminant analysis was applied to classify images into two categories: Normal/inflammation/CIN 1 and CIN 2/CIN 3. The algorithm was applied to 152 images (74 patients) obtained from an international study.

RESULTS

The numbers of normal/inflammatory/CIN 1/CIN 2/CIN 3 images are 74, 29, 14, 24, and 11, respectively. Tenfold cross-validation predicted the algorithm achieved a sensitivity of 51% (95% CI: 36%-67%) and a specificity of 92% (95% CI: 86%-96%) with an empirical two-category prior probability estimated from the data set. Receiver operating characteristic analysis yielded an area under the curve of 0.86.

CONCLUSIONS

The diagnostic efficacy of CADx in OCT imaging to differentiate high-grade CIN from normal/low grade CIN is demonstrated. The high specificity of OCT with CADx suggests further investigation as an effective secondary screening tool when combined with a highly sensitive primary screening tool.

Practical aspects of OCT imaging in tissue engineering

Optical coherence tomography (OCT) is a non-destructive, non-invasive imaging modality conceptually similar to ultrasound imaging but uses near-infrared radiation rather than sound. It is attracting interest throughout the medical community as a tool for ophthalmic scanning (especially of the retina) and potentially for the diagnosis of many other illnesses such as epithelial cancer, connective tissue disorders, and atherosclerosis, as well as for surgical guidance. More recently, it has begun to be explored as a tool for the real-time monitoring of the growth and development of tissue-engineered products. OCT has certain unique advantages over traditional confocal microscopy; in particular, it can image to depths measured in hundreds of microns rather than tens of microns in intact biological tissues and with working distances in excess of 1 cm. Also it possesses label-free contrast for imaging ordered collagen (via birefringence), flow velocity and local shear-rate (via Doppler shifts), and sub-cellular structure (via coherent speckle contrast). The purpose of this short review is to introduce OCT technology and also give guidelines on its practical implementation to the interested researcher.

Towards robust cellular image classification: theoretical foundations for wide-angle scattering pattern analysis

Clinical analysis of light scattering from cellular organelle distributions can help identify disease and predict a patient's response to treatment. This work presents a theoretical basis for the identification of important intracellular distributions from scattering patterns even in the presence of optical and structural variability, and examines how the geometry of an organelle distribution affects key properties of wide-angle (two-dimensional) scattering patterns. Specifically, this work demonstrates how organelle arrangement relates to the size and shape of intensity peaks within simulated scattering images, and how this relationship can affect cell identification when using standard image classification methods.

Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula

Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions--SEADs (Symptomatic Exudate-Associated Derangements)--in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 +/- 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and--together with an anatomical atlas of normal retinas--can be used for clinically important applications.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Three Dimensional OCT in the Engineering of Tissue Constructs: A Potentially Powerful Tool for Assessing Optimal Scaffold Structure

Optical Coherence Tomography (OCT) provides detailed, real-time information on the structure and composition of constructs used in tissue engineering. The focus of this work is the OCT three-dimensional assessment of scaffolding architecture and distribution of cells on it. PLGA scaffolds were imaged in two and three-dimensions, both seeded and unseeded with cells. Then two types of scaffolds were reconstructed in three dimensions. Both scaffolding types were examined at three different seeding densities. The importance of three-dimensional assessments was evident, particularly with respect to porosity and identification of asymmetrical cell distribution.

Towards automated classification of clinical optical coherence tomography data of dense tissues

The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.

Plastinated tissue samples as three-dimensional models for optical instrument characterization

Histology of biological specimens is largely limited to investigating two-dimensional structure because of the sectioning required to produce optically thin samples for conventional microscopy. With the advent of three-dimensional optical imaging technologies such as optical coherence tomography (OCT), diffuse optical tomography (DOT), and multiphoton microscopy (MPM), methods of tissue preparation that minimally disrupt three-dimensional structure are needed. We propose plastination as a means of transforming tissues into three-dimensional models suitable for optical instrument characterization. Tissues are plastinated by infusing them with transparent polymers, after which they can be safely handled, unlike fresh or fixed tissues. Such models are useful for investigating three-dimensional structure, testing and comparing the performance of optical instruments, and potentially investigating tissue properties not normally observed after the three-dimensional scattering properties of a biological samples are lost. We detail our plastination procedures and show examples of imaging several plastinated tissues from a pre-clinical rat model using optical coherence tomography.

Signal-to-noise ratio analysis of all-fiber common-path optical coherence tomography

We present theoretical analysis and experimental verification of the signal to noise ratio (SNR) of a common-path interferometer-based optical coherence tomography (OCT) system. Based on fully integrated all-fiber implementation of a common-path time-domain OCT system, we derived the SNR of the system including the effect of beat noise, which turns out to be twice as large as the excess noise term. We verified the theoretical SNR through a series of experiments, utilizing both controlled phantom and biological samples such as a rat brain with tumor and a frog retina. The results showed that the source power and the reference reflectivity can be easily controlled to optimize the SNR of OCT imaging. We have also analyzed the effect of the fiber delays and the offset in the fiber autocorrelator of the common-path OCT system on the overall SNR.

Deformable and durable phantoms with controlled density of scatterers

We have developed deformable and durable optical tissue phantoms with a simple and well-defined microstructure including a novel combination of scatterers and a matrix material. These were developed for speckle and elastography investigations in optical coherence tomography, but should prove useful in many other fields. We present in detail the fabrication process which involves embedding silica microspheres in a silicone matrix. We also characterize the resulting phantoms with scanning electron microscopy and optical measurements. To our knowledge, no such phantoms were proposed in the literature before. Our technique has a wide range of applicability and could also be adapted to fabricate phantoms with various optical and mechanical properties.

Targeting spectral signatures of progressively dysplastic stratified epithelia using angularly variable fiber geometry in reflectance Monte Carlo simulations

A key component of accurate spectroscopic-based cancer diagnostics is the ability to differentiate spectral variations resulting from epithelial tissue dysplasia. Such measurement may be enhanced by discretely probing the optical properties of the epithelial tissue where the morphological and biochemical features vary according to tissue depths. More precisely, layer-specific changes in tissue optical properties correlated to cellular dysplasia can be determined by conventional reflectance spectroscopy when it is coupled with angularly variable fiber geometry. Thus, this study addresses how angularly variable fiber geometry can resolve spatially specific spectral signatures of tissue pathology by interpreting and analyzing the reflectance spectra of increasingly dysplastic epithelial tissue in reflectance-mode Monte Carlo simulation. Specifically, by increasing the obliquity of the collection fibers from 0 to 40 deg in the direction facing toward the illumination fiber, the spectral sensitivity to tissue abnormalities in the epithelial layer is thereby improved, whereas orthogonal fibers are more sensitive to the changes in the stromal layer.

Experimental evaluation of angularly variable fiber geometry for targeting depth-resolved reflectance from layered epithelial tissue phantoms

The present study focuses on enhancing the sensitivity and specificity of spectral diagnosis in a stratified architecture that models human cervical epithelia by experimentally demonstrating the efficacy of using angularly variable fiber geometry to achieve the desired layer selection and probing depths. The morphological and biochemical features of epithelial tissue vary in accordance with tissue depths; consequently, the accuracy of spectroscopic diagnosis of epithelial dysplasia may be enhanced by probing the optical properties of this tissue. In the case of cellular dysplasia, layer-specific changes in tissue optical properties may be optimally determined by reflectance spectroscopy when specifically coupled with angularly variable fiber geometry. This study addresses the utility of using such angularly variable fiber geometry for resolving spatially specific spectra of a two-layer epithelial tissue phantom. Spectral sensitivity to the scattering particles embedded in the epithelial phantom layer is shown to significantly improve as the obliquity of the collection fibers increases from 0 to 40 deg. Conversely, the orthogonal fibers are found to be more sensitive to changes in the stromal phantom layer.

On-line characterization of YBCO coated conductors using Raman spectroscopy methods

The use of Raman spectroscopy for on-line monitoring of the production of superconducting YBa2Cu3O6+X (YBCO) thin films on long-length metal tapes coated with textured buffer layers is reported for the first time. A methodology is described for obtaining Raman spectra of YBCO on moving tape exiting a metal-organic-chemical-vapor-deposition (MOCVD) enclosure. After baseline correction, the spectra recorded in this way show the expected phonons of the specific YBCO crystal orientation required for high supercurrent transport, as well as phonons of non-superconducting second-phase impurities when present. It is also possible to distinguish YBCO films that are properly textured from films having domains of misoriented YBCO grains. An investigation of the need for focus control on moving tape indicated that focusing of the laser on the surface of the highly reflective YBCO films exiting the MOCVD enclosure tends to produce aberrant photon bursts that swamp the Raman spectrum. These photon bursts are very likely a consequence of optical speckle effects induced by a combination of surface roughness, crystallographic texture, and/or local strain within the small grain microstructure of the YBCO film. Maintaining a slightly out-of-focus condition provides the best signal-to-noise ratio in terms of the obtained Raman spectra. In addition to examining moving tape at the post-MOCVD stage, Raman spectra of the film surface can also be recorded after the oxygen anneal performed to bring the YBCO to the optimum superconducting state. Consideration is given to data processing methods that could be adapted to the on-line Raman spectra to allow the tagging of out-of-specification tape segments and, at a more advanced level, feedback control to the MOCVD process.