Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging

Semi-supervised assisted multi-task learning for oral optical coherence tomography image segmentation and denoising

Optical coherence tomography (OCT) is promising to become an essential imaging tool for non-invasive oral mucosal tissue assessment, but it faces challenges like speckle noise and motion artifacts. In addition, it is difficult to distinguish different layers of oral mucosal tissues from gray level OCT images due to the similarity of optical properties between different layers. We introduce the Efficient Segmentation-Denoising Model (ESDM), a multi-task deep learning framework designed to enhance OCT imaging by reducing scan time from ∼8s to ∼2s and improving oral epithelium layer segmentation. ESDM integrates the local feature extraction capabilities of the convolution layer and the long-term information processing advantages of the transformer, achieving better denoising and segmentation performance compared to existing models. Our evaluation shows that ESDM outperforms state-of-the-art models with a PSNR of 26.272, SSIM of 0.737, mDice of 0.972, and mIoU of 0.948. Ablation studies confirm the effectiveness of our design, such as the feature fusion methods, which enhance performance with minimal model complexity increase. ESDM also presents high accuracy in quantifying oral epithelium thickness, achieving mean absolute errors as low as 5 µm compared to manual measurements. This research shows that ESDM can notably improve OCT imaging and reduce the cost of accurate oral epithermal segmentation, improving diagnostic capabilities in clinical settings.

A Survey on Optical Coherence Tomography-Technology and Application

This paper reviews the main research on Optical Coherence Tomography (OCT), focusing on the progress and advancements made by researchers over the past three decades in its methods and medical imaging applications. By analyzing existing studies and developments, this review aims to provide a foundation for future research in the field.

Score Prior Guided Iterative Solver for Speckles Removal in Optical Coherent Tomography Images

Optical coherence tomography (OCT) is a widely used non-invasive imaging modality for ophthalmic diagnosis. However, the inherent speckle noise becomes the leading cause of OCT image quality, and efficient speckle removal algorithms can improve image readability and benefit automated clinical analysis. As an ill-posed inverse problem, it is of utmost importance for speckle removal to learn suitable priors. In this work, we develop a score prior guided iterative solver (SPIS) with logarithmic space to remove speckles in OCT images. Specifically, we model the posterior distribution of raw OCT images as a data consistency term and transform the speckle removal from a nonlinear into a linear inverse problem in the logarithmic domain. Subsequently, the learned prior distribution through the score function from the diffusion model is utilized as a constraint for the data consistency term into the linear inverse optimization, resulting in an iterative speckle removal procedure that alternates between the score prior predictor and the subsequent non-expansive data consistency corrector. Experimental results on the private and public OCT datasets demonstrate that the proposed SPIS has an excellent performance in speckle removal and out-of-distribution (OOD) generalization. Further downstream automatic analysis on the OCT images verifies that the proposed SPIS can benefit clinical applications.

Denoising in optical coherence tomography volumes for improved 3D visualization

Optical coherence tomography (OCT) has already become one of the most important diagnostic tools in different fields of medicine, as well as in various industrial applications. The most important characteristic of OCT is its high resolution, both in depth and the transverse direction. Together with the information on the tissue density, OCT offers highly precise information on tissue geometry. However, the detectability of small and low-intensity features in OCT scans is limited by the presence of speckle noise. In this paper we present a new volumetric method for noise removal in OCT volumes, which aims at improving the quality of rendered 3D volumes. In order to remove noise uniformly, while preserving important details, the proposed algorithm simultaneously observes the estimated amounts of noise and the sharpness measure, and iteratively enhances the volume until it reaches the required quality. We evaluate the proposed method using four quality measures as well as visually, by evaluating the visualization of OCT volumes on an auto-stereoscopic 3D screen. The results show that the proposed method outperforms reference methods both visually and in terms of objective measures.

Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling

Background

Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view.

Methods

In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator.

Results

The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors.

Conclusion

The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.

Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct an important theoretical error in the previously reported results related to OCT image reconstruction using the Non-uniform Discrete Fourier Transform (NDFT). Moreover, we describe an efficient method to compute our corrected reconstruction transform, i.e., a scaled NDFT, using the Fast Fourier Transform (FFT). Finally, we demonstrate different advantages of our generalized OCT image reconstruction method by achieving (1) theoretically corrected OCT image reconstruction directly from non-uniformly spaced frequency domain samples; (2) a novel OCT image reconstruction method with a higher signal-to-noise ratio (SNR) using redundant frequency domain samples. Our new image reconstruction method is an improvement of OCT technology, so it could benefit all OCT applications.

Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach

Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.

Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images

SIGNIFICANCE

Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue.

AIM

We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume.

APPROACH

The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities.

RESULTS

The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information.

CONCLUSIONS

The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images

Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extract deeper information from the original OCT images. In addition, we use dilated convolution and residual learning to increase the learning ability of our DPNet. We use 100 pairs of human retinal OCT images as the training dataset. Then we test the DPNet model for denoising speckles on four different types of OCT images, mainly including human retinal OCT images, skin OCT images, colon crypt OCT images, and quail embryo OCT images. We compare the DPNet model with the adaptive complex diffusion method, the curvelet shrinkage method, the shearlet-based total variation method, and the OCTNet method. We qualitatively and quantitatively evaluate these methods in terms of image smoothness, structural information protection, and edge clarity. Our experimental results prove the performance of the DPNet model, and it allows us to batch and quickly process different types of poor-quality OCT images without any parameter fine-tuning under a time-constrained situation.

Detection of Neurological and Ophthalmological Pathologies with Optical Coherence Tomography Using Retinal Thickness Measurements: A Bibliometric Study

We carry out a bibliometric analysis on neurological and ophthalmological pathologies based on retinal nerve fiber layer (RNFL) thickness measured with optical coherence tomography (OCT). Documents were selected from Scopus database. We have applied the most commonly used bibliometric indicators, both for production and dispersion, as Price’s law of scientific literature growth, Lotka’s law, the transient index, and the Bradford model. Finally, the participation index of the different countries and affiliations was calculated. Two-hundred-and-forty-one documents from the period 2000–2019 were retrieved. Scientific production was better adjusted to linear growth (r = 0.88) than exponential (r = 0.87). The duplication time of the documents obtained was 5.6 years. The transience index was 89.62%, which indicates that most of the scientific production is due to very few authors. The signature rate per document was 5.2. Nine journals made up the Bradford core. USA and University of California present the highest production. The most frequently discussed topics on RNFL thinning are glaucoma and neurodegenerative diseases (NDD). The growth of the scientific literature on RNFL thickness was linear, with a very high rate of transience, which indicates low productivity and the presence of numerous authors who sporadically publish on this topic. No evidence of a saturation point was observed. In the last 10 years, there has been an increase in documents relating the decline of RNFL to NDD.

Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography

Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.

2D MEMS-based high-speed beam-shifting technique for speckle noise reduction and flow rate measurement in optical coherence tomography

In this manuscript, a two-dimensional (2D) micro-electro-mechanical system (MEMS)-based, high-speed beam-shifting spectral domain optical coherence tomography (MHB-SDOCT) is proposed for speckle noise reduction and absolute flow rate measurement. By combining a zigzag scanning protocol, the frame rates of 45.2 Hz for speckle reduction and 25.6 Hz for flow rate measurement are achieved for in-vivo tissue imaging. Phantom experimental results have shown that by setting the incident beam angle to ϕ = 4.76° (between optical axis of objective lens and beam axis) and rotating the beam about the optical axis in 17 discrete angular positions, 91% of speckle noise in the structural images can be reduced. Furthermore, a precision of 0.0032 µl/s is achieved for flow rate measurement with the same beam angle, using three discrete angular positions around the optical axis. In-vivo experiments on human skin and chicken embryo were also implemented to further verify the performance of speckle noise reduction and flow rate measurement of MHB-SDOCT.

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.

An overview of methods to mitigate artifacts in optical coherence tomography imaging of the skin

BACKGROUND

Optical coherence tomography (OCT) of skin delivers three-dimensional images of tissue microstructures. Although OCT imaging offers a promising high-resolution modality, OCT images suffer from some artifacts that lead to misinterpretation of tissue structures. Therefore, an overview of methods to mitigate artifacts in OCT imaging of the skin is of paramount importance. Speckle, intensity decay, and blurring are three major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT. Intensity decay is a deterioration of light with respect to depth, and blurring is the consequence of deficiencies of optical components.

METHOD

Two speckle reduction methods (one based on artificial neural network and one based on spatial compounding), an attenuation compensation algorithm (based on Beer-Lambert law) and a deblurring procedure (using deconvolution), are described. Moreover, optical properties extraction algorithm based on extended Huygens-Fresnel (EHF) principle to obtain some additional information from OCT images are discussed.

RESULTS

In this short overview, we summarize some of the image enhancement algorithms for OCT images which address the abovementioned artifacts. The results showed a significant improvement in the visibility of the clinically relevant features in the images. The quality improvement was evaluated using several numerical assessment measures.

CONCLUSION

Clinical dermatologists benefit from using these image enhancement algorithms to improve OCT diagnosis and essentially function as a noninvasive optical biopsy.

Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy

Assessment of serous retinal detachment plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an automatic, three-dimensional segmentation method to detect both neurosensory retinal detachment (NRD) and pigment epithelial detachment (PED) in spectral domain optical coherence tomography (SD-OCT) images. The proposed method involves constructing a probability map from training samples using random forest classification. The probability map is constructed from a linear combination of structural texture, intensity, and layer thickness information. Then, a continuous max flow optimization algorithm is applied to the probability map to segment the retinal detachment-associated fluid regions. Experimental results from 37 retinal SD-OCT volumes from cases of CSC demonstrate the proposed method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), positive predicative value (PPV), and dice similarity coefficient (DSC) of 92.1%, 0.53%, 94.7%, and 93.3%, respectively, for NRD segmentation and 92.5%, 0.14%, 80.9%, and 84.6%, respectively, for PED segmentation. The proposed method can be an automatic tool to evaluate serous retinal detachment and has the potential to improve the clinical evaluation of CSC.

Optical coherence tomography image denoising using Gaussianization transform

We demonstrate the power of the Gaussianization transform (GT) for modeling image content by applying GT for optical coherence tomography (OCT) denoising. The proposed method is a developed version of the spatially constrained Gaussian mixture model (SC-GMM) method, which assumes that each cluster of similar patches in an image has a Gaussian distribution. SC-GMM tries to find some clusters of similar patches in the image using a spatially constrained patch clustering and then denoise each cluster by the Wiener filter. Although in this method GMM distribution is assumed for the noisy image, holding this assumption on a dataset is not investigated. We illustrate that making a Gaussian assumption on a noisy dataset has a significant effect on denoising results. For this purpose, a suitable distribution for OCT images is first obtained and then GT is employed to map this original distribution of OCT images to a GMM distribution. Then, this Gaussianized image is used as the input of the SC-GMM algorithm. This method, which is a combination of GT and SC-GMM, remarkably improves the results of OCT denoising compared with earlier version of SC-GMM and even produces better visual and numerical results than the state-of-the art works in this field. Indeed, the main advantage of the proposed OCT despeckling method is texture preservation, which is important for main image processing tasks like OCT inter- and intraretinal layer analysis. Thus, to prove the efficacy of the proposed method for this analysis, an improvement in the segmentation of intraretinal layers using the proposed method as a preprocessing step is investigated. Furthermore, the proposed method can achieve the best expert ranking between other contending methods, and the results show the helpfulness and usefulness of the proposed method in clinical applications.

Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography

Optical coherence tomography (OCT) is based on coherence detection of interferometric signals and hence inevitably suffers from speckle noise. To remove speckle noise in OCT images, wavelet domain thresholding has demonstrated significant advantages in suppressing noise magnitude while preserving image sharpness. However, speckle noise in OCT images has different characteristics in different spatial scales, which has not been considered in previous applications of wavelet domain thresholding. In this study, we demonstrate a noise adaptive wavelet thresholding (NAWT) algorithm that exploits the difference of noise characteristics in different wavelet sub-bands. The algorithm is simple, fast, effective and is closely related to the physical origin of speckle noise in OCT image. Our results demonstrate that NAWT outperforms conventional wavelet thresholding.

Multifiber angular compounding optical coherence tomography for speckle reduction

We report on an integrated fiber optic design to implement multifiber angular compounding optical coherence tomography, which enables angular compounding for speckle reduction. A multi-facet fiber array delivers three light beams to the sample with different incident angles. Back-reflective/back-scattered signals from these channels were simultaneously detected by a three-channel spectrometer. The axial and lateral resolution was measured to be ∼3 and ∼3.5  μm, respectively, in air with ∼100  dB sensitivity. We conducted ex vivo experiments on a rat esophagus to demonstrate a contrast to noise improvement of 1.58.

Assessment of corneal properties based on statistical modeling of OCT speckle

A new approach to assess the properties of the corneal micro-structure in vivo based on the statistical modeling of speckle obtained from Optical Coherence Tomography (OCT) is presented. A number of statistical models were proposed to fit the corneal speckle data obtained from OCT raw image. Short-term changes in corneal properties were studied by inducing corneal swelling whereas age-related changes were observed analyzing data of sixty-five subjects aged between twenty-four and seventy-three years. Generalized Gamma distribution has shown to be the best model, in terms of the Akaike's Information Criterion, to fit the OCT corneal speckle. Its parameters have shown statistically significant differences (Kruskal-Wallis, p < 0.001) for short and age-related corneal changes. In addition, it was observed that age-related changes influence the corneal biomechanical behaviour when corneal swelling is induced. This study shows that Generalized Gamma distribution can be utilized to modeling corneal speckle in OCT in vivo providing complementary quantified information where micro-structure of corneal tissue is of essence.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

Optical Coherence Tomographic Elastography Reveals Mesoscale Shear Strain Inhomogeneities in the Annulus Fibrosus

STUDY DESIGN

Basic science study using in vitro tissue testing and imaging to characterize local strains in annulus fibrosus (AF) tissue.

OBJECTIVE

To characterize mesoscale strain inhomogeneities between lamellar and inter-/translamellar (ITL) matrix compartments during tissue shear loading.

SUMMARY OF BACKGROUND DATA

The intervertebral disc is characterized by significant heterogeneities in tissue structure and plays a critical role in load distribution and force transmission in the spine. In particular, the AF possesses a lamellar architecture interdigitated by a complex network of extracellular matrix components that form a distinct ITL compartment. Currently, there is not a firm understanding of how the lamellar and ITL matrix coordinately support tissue loading.

METHODS

AF tissue samples were prepared from frozen porcine lumbar spines and mounted onto custom fixtures of a materials testing system that incorporates optical coherence tomography (OCT) imaging to perform tissue elastography. Tissues were subjected to 20 and 40% nominal shear strain, and OCT images were captured and segmented to identify regions of interest corresponding to lamellar and ITL compartments. Images were analyzed using an optical flow algorithm to quantify local shear strains within each compartment.

RESULTS

Using histology and OCT, we first verified our ability to visualize and discriminate the ITL matrix from the lamellar matrix in porcine AF tissues. Local AF strains in the ITL compartment (22.0 ± 13.8, 31.1 ± 16.9 at 20% and 40% applied shear, respectively) were significantly higher than corresponding strains in the surrounding lamellar compartment (12.1 ± 5.6, 15.3 ± 5.2) for all tissue samples (P < 0.05).

CONCLUSION

Results from this study demonstrate that the lamellar and ITL compartments of the AF distribute strain unevenly during tissue loading. Specifically, shear strain is significantly higher in the ITL matrix, suggesting that these regions may be more susceptible to tissue damage and more mechanobiologically active.

LEVEL OF EVIDENCE

N/A.

Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method's performance was assessed in comparison with observer-defined standards using clinical pullback data. Values (calcium [Formula: see text], lipid [Formula: see text], and fibrous [Formula: see text]) were consistent with previous measurements obtained by other means. Using optical parameters ([Formula: see text], [Formula: see text], [Formula: see text]), we achieved feature space separation of plaque types and classification accuracy of [Formula: see text]. Despite the rapid [Formula: see text] motion and varying incidence angle in pullbacks, the proposed computational pipeline appears to work as well as a more standard "stationary" approach.

Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images

Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging--a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patch-based methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patch-based methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others.

Enhancement of OCT images with vinyl polysiloxane (VPS)

Several studies have shown that optical coherence tomography (OCT) can be used to measure the remaining enamel thickness and detect the location of subsurface lesions hidden under the sound enamel. Moreover studies have shown that high refractive index liquids can be used to improve the visibility of subsurface lesions in OCT images. In this study, we demonstrate that vinyl polysiloxane (VPS) impression materials which are routinely used in dentistry can be used to enhance the detection of dentinal lesions on tooth occlusal surfaces. Lesion presence was confirmed with polarized light microscopy and microradiography.

Three dimensional data-driven multi scale atomic representation of optical coherence tomography

In this paper, we discuss about applications of different methods for decomposing a signal over elementary waveforms chosen in a family called a dictionary (atomic representations) in optical coherence tomography (OCT). If the representation is learned from the data, a nonparametric dictionary is defined with three fundamental properties of being data-driven, applicability on 3D, and working in multi-scale, which make it appropriate for processing of OCT images. We discuss about application of such representations including complex wavelet based K-SVD, and diffusion wavelets on OCT data. We introduce complex wavelet based K-SVD to take advantage of adaptability in dictionary learning methods to improve the performance of simple dual tree complex wavelets in speckle reduction of OCT datasets in 2D and 3D. The algorithm is evaluated on 144 randomly selected slices from twelve 3D OCTs taken by Topcon 3D OCT-1000 and Cirrus Zeiss Meditec. Improvement of contrast to noise ratio (CNR) (from 0.9 to 11.91 and from 3.09 to 88.9, respectively) is achieved. Furthermore, two approaches are proposed for image segmentation using diffusion. The first method is designing a competition between extended basis functions at each level and the second approach is defining a new distance for each level and clustering based on such distances. A combined algorithm, based on these two methods is then proposed for segmentation of retinal OCTs, which is able to localize 12 boundaries with unsigned border positioning error of 9.22 ±3.05 μm, on a test set of 20 slices selected from 13 3D OCTs.

Enhanced detection of dentinal lesions in OCT images using the RKT transformation

Several studies have shown that optical coherence tomography (OCT) can be used to measure the remaining enamel thickness and detect the location of subsurface lesions hidden under the sound enamel. The purpose of this study was to develop algorithms to enhance the visibility of subsurface structures such as hidden occlusal lesions and the dentinal-enamel junction. Extracted teeth with natural occlusal lesions were imaged with OCT with and without added high index fluids. A Rotating Kernel Transformation (RKT) nonlinear image processing filter was applied to PS-OCT images to enhance the visibility of the subsurface lesions under the sound enamel. The filter significantly increased (P<0.05) the visibility of the subsurface lesions.

Speckle reduction in optical coherence tomography by image registration and matrix completion

Speckle noise is problematic in optical coherence tomography (OCT). With the fast scan rate, swept source OCT scans the same position in the retina for multiple times rapidly and computes an average image from the multiple scans for speckle reduction. However, the eye movement poses some challenges. In this paper, we propose a new method for speckle reduction from multiply-scanned OCT slices. The proposed method applies a preliminary speckle reduction on the OCT slices and then registers them using a global alignment followed by a local alignment based on fast iterative diamond search. After that, low rank matrix completion using bilateral random projection is utilized to iteratively estimate the noise and recover the underlying clean image. Experimental results show that the proposed method achieves average contrast to noise ratio 15.65, better than 13.78 by the baseline method used currently in swept source OCT devices. The technology can be embedded into current OCT machines to enhance the image quality for subsequent analysis.

Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography

This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.

Evaluation of various speckle reduction filters on medical ultrasound images

At present, ultrasound is one of the essential tools for noninvasive medical diagnosis. However, speckle noise is inherent in medical ultrasound images and it is the cause for decreased resolution and contrast-to-noise ratio. Low image quality is an obstacle for effective feature extraction, recognition, analysis, and edge detection; it also affects image interpretation by doctor and the accuracy of computer-assisted diagnostic techniques. Thus, speckle reduction is significant and critical step in pre-processing of ultrasound images. Many speckle reduction techniques have been studied by researchers, but to date there is no comprehensive method that takes all the constraints into consideration. In this paper we discuss seven filters, namely Lee, Frost, Median, Speckle Reduction Anisotropic Diffusion (SRAD), Perona-Malik's Anisotropic Diffusion (PMAD) filter, Speckle Reduction Bilateral Filter (SRBF) and Speckle Reduction filter based on soft thresholding in the Wavelet transform. A comparative study of these filters has been made in terms of preserving the features and edges as well as effectiveness of de-noising.We computed five established evaluation metrics in order to determine which despeckling algorithm is most effective and optimal for real-time implementation. In addition, the experimental results have been demonstrated by filtered images and statistical data table.

Speckle reduction in optical coherence tomography images based on wave atoms

Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques.

Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT

In this paper we present a new process for assessing optical properties of tissues from 3D pullbacks, the standard clinical acquisition method for iOCT data. Our method analyzes a volume of interest (VOI) consisting of about 100 A-lines spread across the angle of rotation (θ) and along the artery, z. The new 3D method uses catheter correction, baseline removal, speckle noise reduction, alignment of A-line sequences, and robust estimation. We compare results to those from a more standard, "gold standard" stationary acquisition where many image frames are averaged to reduce noise. To do these studies in a controlled fashion, we use a realistic optical artery phantom containing of multiple "tissue types." Precision and accuracy for 3D pullback analysis are reported. Our results indicate that when implementing the process on a stationary acquisition dataset, the uncertainty improves at each stage while the uncertainty is reduced. When comparing stationary acquisition dataset to pullback dataset, the values were as follows: calcium: 3.8±1.09mm^-1 in stationary and 3.9±1.2 mm^-1 in a pullback; lipid: 11.025±0.417 mm^-1 in stationary and 11.27±0.25 mm^-1 in pullback; fibrous: 6.08±1.337 mm^-1 in stationary and 5.58±2.0 mm^-1 . These results indicates that the process presented in this paper introduce minimal bias and only a small change in uncertainty when comparing a stationary and pullback dataset, thus paves the way to a highly accurate clinical plaque type discrimination, enabling automatic classification.

Speckle reduction in optical coherence tomography by matrix completion using bilateral random projection

Speckle noise is problematic in optical coherence tomography (OCT) and often obscures the structure details. In this paper, we propose a new method to reduce speckle noise from multiply scanned OCT slices. The proposed method registers the OCT scans using a global alignment followed by a local alignment based on global and local motion estimation. Then low rank matrix completion using bilateral random projection is utilized to estimate the noise and recover the clean image. Experimental results show that the proposed method archives average contrast to noise ratio 14.90, better than 13.78 by the state-of-the-art method used in current OCT machines. The technology can be embedded into current OCT machines to enhance the image quality.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Background

Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses.

Method

An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object.

Results

The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1.

Conclusions

In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Speckle reduction using an artificial neural network algorithm

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images.

Assessment of frame-averaging algorithms in OCT image analysis

BACKGROUND AND OBJECTIVE

To evaluate frame registration and averaging algorithms for optical coherence tomography.

PATIENTS AND METHODS

Twenty normal and 20 glaucomatous eyes were imaged. Objective differences were measured by comparing noise variance, spread of edge, and contrast-to-noise ratio. Subjective image quality was also evaluated.

RESULTS

Two frame-averaging algorithms (FA400 and FA407) had better noise variance and contrast-to-noise ratio but worse spread of edge than did single frames (P < .01). Both algorithms provided better subjective assessments of structure boundaries than did single images (P < .001). FA407 had significantly lower spread of edge and better internal limiting membrane visualization than FA400.

CONCLUSION

Frame averaging significantly suppressed speckle noise and increased the visibility of retinal structures, but imperfect image registration caused edge blurring that could be detected by the spread of edge parameter. In frame-averaging algorithms, higher contrast-to-noise ratio and lower noise variance indicated better noise suppression, but spread of edge was most sensitive in comparing edge preservation between algorithms.

Automated drusen segmentation and quantification in SD-OCT images

Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome.

Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image

Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.

3-D adaptive nonlinear complex-diffusion despeckling filter

This work aims to improve the process of speckle noise reduction while preserving edges and other relevant features through filter expansion from 2-D to 3-D. Despeckling is very important for data visual inspection and as a preprocessing step for other algorithms, as they are usually notably influenced by speckle noise. To that intent, a 3-D approach is proposed for the adaptive complex-diffusion filter. This 3-D iterative filter was applied to spectral-domain optical coherence tomography medical imaging volumes of the human retina and a quantitative evaluation of the results was performed to allow a demonstration of the better performance of the 3-D over the 2-D filtering and to choose the best total diffusion time. In addition, we propose a fast graphical processing unit parallel implementation so that the filter can be used in a clinical setting.

Speckle reduction of endovascular optical coherence tomography using a generalized divergence measure

Endovascular optical coherence tomography (EV-OCT) is an emerging intravascular imaging technique for observing blood vessel walls. Fluctuating speckle noise, especially during rapid pull-back, can severely degrade the visibility of morphological structures. Moreover, the speckle pattern varies in different parts of the image due to beam divergence and is further complicated by interpolation through the coordinate transformation necessary for displaying the rotary scanning images, challenging the use of frequency domain analysis. In this study, a computationally efficient method using a generalized divergence regularization procedure is presented to suppress speckle noise in EV-OCT images. Results show substantial smoothing of the grainy appearance and enhanced visualization of deeper structures as demonstrated in porcine carotid arteries.

Efficient reduction of speckle noise in Optical Coherence Tomography

Speckle pattern, which is inherent in coherence imaging, influences significantly axial and transversal resolution of Optical Coherence Tomography (OCT) instruments. The well known speckle removal techniques are either sensitive to sample motion, require sophisticated and expensive sample tracking systems, or involve sophisticated numerical procedures. As a result, their applicability to in vivo real-time imaging is limited. In this work, we propose to average multiple A-scans collected in a fully controlled way to reduce the speckle contrast. This procedure involves non-coherent averaging of OCT A-scans acquired from adjacent locations on the sample. The technique exploits scanning protocol with fast beam deflection in the direction perpendicular to lateral dimension of the cross-sectional image. Such scanning protocol reduces the time interval between A-scans to be averaged to the repetition time of the acquisition system. Consequently, the averaging algorithm is immune to bulk motion of an investigated sample, does not require any sophisticated data processing to align cross-sectional images, and allows for precise control of lateral shift of the scanning beam on the object. The technique is tested with standard Spectral OCT system with an extra resonant scanner used for rapid beam deflection in the lateral direction. Ultrahigh speed CMOS camera serves as a detector and acquires 200,000 spectra per second. A dedicated A-scan generation algorithm allows for real-time display of images with reduced speckle contrast at 6 frames/second. This technique is applied to in vivo imaging of anterior and posterior segments of the human eye and human skin.

OPTICAL COHERENCE TOMOGRAPHY HEART TUBE IMAGE DENOISING BASED ON CONTOURLET TRANSFORM

Optical Coherence Tomography(OCT) gradually becomes a very important imaging technology in the Biomedical field for its noninvasive, nondestructive and real-time properties. However, the interpretation and application of the OCT images are limited by the ubiquitous noise. In this paper, a denoising algorithm based on contourlet transform for the OCT heart tube image is proposed. A bivariate function is constructed to model the joint probability density function (pdf) of the coefficient and its cousin in contourlet domain. A bivariate shrinkage function is deduced to denoise the image by the maximum a posteriori (MAP) estimation. Three metrics, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and equivalent number of look (ENL), are used to evaluate the denoised image using the proposed algorithm. The results show that the signal-to-noise ratio is improved while the edges of object are preserved by the proposed algorithm. Systemic comparisons with other conventional algorithms, such as mean filter, median filter, RKT filter, Lee filter, as well as bivariate shrinkage function for wavelet-based algorithm are conducted. The advantage of the proposed algorithm over these methods is illustrated.

Optical coherence tomography-current technology and applications in clinical and biomedical research

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides real-time two- and three-dimensional images of scattering samples with micrometer resolution. By mapping the local reflectivity, OCT visualizes the morphology of the sample. In addition, functional properties such as birefringence, motion, or the distributions of certain substances can be detected with high spatial resolution. Its main field of application is biomedical imaging and diagnostics. In ophthalmology, OCT is accepted as a clinical standard for diagnosing and monitoring the treatment of a number of retinal diseases, and OCT is becoming an important instrument for clinical cardiology. New applications are emerging in various medical fields, such as early-stage cancer detection, surgical guidance, and the early diagnosis of musculoskeletal diseases. OCT has also proven its value as a tool for developmental biology. The number of companies involved in manufacturing OCT systems has increased substantially during the last few years (especially due to its success in opthalmology), and this technology can be expected to continue to spread into various fields of application.

Improved adaptive complex diffusion despeckling filter

Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.

General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery

An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired in-vivo with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.

Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging

Optical coherence tomography (OCT) is rapidly becoming the method of choice for assessing arterial wall pathology in vivo. Atherosclerotic plaques can be diagnosed with high accuracy, including measurement of the thickness of fibrous caps, enabling an assessment of the risk of rupture. While the OCT image presents morphological information in highly resolved detail, it relies on interpretation of the images by trained readers for the identification of vessel wall components and tissue type. We present a framework to enable systematic and automatic classification of atherosclerotic plaque constituents, based on the optical attenuation coefficient mu(t) of the tissue. OCT images of 65 coronary artery segments in vitro, obtained from 14 vessels harvested at autopsy, are analyzed and correlated with histology. Vessel wall components can be distinguished based on their optical properties: necrotic core and macrophage infiltration exhibit strong attenuation, mu(t)>or=10 mm(-1), while calcific and fibrous tissue have a lower mu(t) approximately 2-5mm(-1). The algorithm is successfully applied to OCT patient data, demonstrating that the analysis can be used in a clinical setting and assist diagnostics of vessel wall pathology.