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The choroidal vascularity index (CVI) is a new biomarker defined for retinal optical coherence tomography (OCT) images for measuring and evaluating the choroidal vascular structure. The CVI is the ratio of the choroidal luminal area (LA) to the total choroidal area (TCA). The automatic calculation of this index is important for ophthalmologists but has not yet been explored. In this study, we proposed a fully automated method based on deep learning for calculating the CVI in three main steps: 1—segmentation of the choroidal boundary, 2—detection of the choroidal luminal vessels, and 3—computation of the CVI. The proposed method was evaluated in complex situations such as the presence of diabetic retinopathy and pachychoroid spectrum. In pachychoroid spectrum, the choroid is thickened, and the boundary between the choroid and sclera (sclerochoroidal junction) is blurred, which makes the segmentation more challenging. The proposed method was designed based on the U-Net model, and a new loss function was proposed to overcome the segmentation problems. The vascular LA was then calculated using Niblack's local thresholding method, and the CVI value was finally computed. The experimental results for the segmentation stage with the best-performing model and the proposed loss function used showed Dice coefficients of 0.941 and 0.936 in diabetic retinopathy and pachychoroid spectrum patients, respectively. The unsigned boundary localization errors in the presence of diabetic retinopathy were 3 and 20.7 μm for the BM boundary and sclerochoroidal junction, respectively. Similarly, the unsigned errors in the presence of pachychoroid spectrum were 21.6 and 76.2 μm for the BM and sclerochoroidal junction, respectively. The performance of the proposed method to calculate the CVI was evaluated; the Bland–Altman plot indicated an acceptable agreement between the values allocated by experts and the proposed method in the presence of diabetic retinopathy and pachychoroid spectrum. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications. [ABSTRACT FROM AUTHOR]

Background: Asymmetry analysis of retinal layers in right and left eyes can be a valuable tool for early diagnoses of retinal diseases. To determine the limits of the normal interocular asymmetry in retinal layers around macula, thickness measurements are obtained with optical coherence tomography (OCT). Methods: For this purpose, after segmentation of intraretinal layer in threedimensional OCT data and calculating the midmacular point, the TM of each layer is obtained in 9 sectors in concentric circles around the macula. To compare corresponding sectors in the right and left eyes, the TMs of the left and right images are registered by alignment of retinal raphe (i.e. diskfovea axes). Since the retinal raphe of macular OCTs is not calculable due to limited region size, the TMs are registered by first aligning corresponding retinal raphe of fundus images and then registration of the OCTs to aligned fundus images. To analyze the asymmetry in each retinal layer, the mean and standard deviation of thickness in 9 sectors of 11 layers are calculated in 50 normal individuals. Results: The results demonstrate that some sectors of retinal layers have signifcant asymmetry with P < 0.05 in normal population. In this base, the tolerance limits for normal individuals are calculated. Conclusion: This article shows that normal population does not have identical retinal information in both eyes, and without considering this reality, normal asymmetry in information gathered from both eyes might be interpreted as retinal disorders. [ABSTRACT FROM AUTHOR]

The recent application of Fourier Based Iterative Reconstruction Method (FIRM) has made it possible to achieve high-quality 2D images from a fan beam Computed Tomography (CT) scan with a limited number of projections in a fast manner. The proposed methodology in this article is designed to provide 3D Radon space in linogram fashion to facilitate the use of FIRM with cone beam projections (CBP) for the reconstruction of 3D images in a sparse view angles Cone Beam CT (CBCT). For this reason, in the first phase, the 3D Radon space is generated using CBP data after discretization and optimization of the famous Grangeat’s formula. The method used in this process involves fast Pseudo Polar Fourier transform (PPFT) based on 2D and 3D Discrete Radon Transformation (DRT) algorithms with no wraparound effects. In the second phase, we describe reconstruction of the objects with available Radon values, using direct inverse of 3D PPFT. The method presented in this section eliminates noises caused by interpolation from polar to Cartesian space and exhibits no thorn, V-shaped and wrinkle artifacts. This method reduces the complexity to O(n3 log n) for images of size $ {{\mathrm {n}}\times {\mathrm {n}} \times {\mathrm {n}}}$. The Cone to Radon conversion (Cone2Radon) Toolbox in the first phase and MATLAB/Python toolbox in the second phase were tested on three digital phantoms and experiments demonstrate fast and accurate cone beam image reconstruction due to proposed modifications in all three stages of Grangeat’s method. [ABSTRACT FROM AUTHOR]

Background: Image fusion is the process of combining the information of several input images into one image. Projection images obtained from three-dimensional (3D) optical coherence tomography (OCT) can show inlier retinal pathology and abnormalities that are not visible in conventional fundus images. In recent years, the projection image is often made by an average on all retina that causes to lose many intraretinal details. Methods: In this study, we focus on the formation of optimum projection images from retinal layers using Curvelet-based image fusion. The latter consists of three main steps. In the earlier studies, macular spectral 3D data using diffusion map-based OCT were segmented into 12 different boundaries identifying 11 retinal layers in three dimensions. In the second step, projection images are attained using conducting some statistical methods on the space between each pair of boundaries. In the next step, retinal layers are merged using Curvelet transform to make the final projection images. Results: These images contain integrated retinal depth information as well as an ideal opportunity to better extract retinal features such as vessels and the macula region. Finally, qualitative and quantitative evaluations show the superiority of this method to the average-based and wavelet-based fusion methods. Overall, our method obtains the best results for image fusion in all terms such as entropy (6.7744) and AG (9.5491). Conclusion: Creating an image with more and detailed information made by the Curvelet-based image fusion has significantly higher contrast. There are also many thin veins in Curvelet-based fused image, which are absent in average-based and wavelet-based fused images. [ABSTRACT FROM AUTHOR]

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar-venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches. [ABSTRACT FROM AUTHOR]

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 \pm 3.05 \mu \ m, on a test set of 20 slices selected from 13 3D OCTs. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the thickness map of eleven retinal layers in normal subjects by spectral domain optical coherence tomography (SD-OCT) and evaluate their association with sex and age. Mean regional retinal thickness of 11 retinal layers was obtained by automatic three-dimensional diffusion map based method in 112 normal eyes of 76 Iranian subjects. We applied our previously reported 3D intraretinal fast layer segmentation which does not require edge-based image information but rather relies on regional image texture. The thickness maps are compared among 9 macular sectors within 3 concentric circles as defined by ETDRS. The thickness map of central foveal area in layers 1, 3, and 4 displayed the minimum thickness. Maximum thickness was observed in nasal to the fovea of layer 1 and in a circular pattern in the parafoveal retinal area of layers 2, 3, and 4 and in central foveal area of layer 6. Temporal and inferior quadrants of the total retinal thickness and most of other quadrants of layer 1 were significantly greater in the men than in the women. Surrounding eight sectors of total retinal thickness and a limited number of sectors in layers 1 and 4 significantly correlated with age. [ABSTRACT FROM AUTHOR]

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively. [ABSTRACT FROM AUTHOR]

The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral domain (SD) OCT machines. A texture-based algorithm is introduced in this paper for fully automatic segmentation of choroidal images obtained from an EDI system of Heidelberg 3D OCT Spectralis. Dynamic programming is utilized to determine the location of the retinal pigment epithelium (RPE). Bruch's membrane (BM) (the blood-retina barrier which separates the RPE cells of the retina from the choroid) can be segmented by searching for the pixels with the biggest gradient value below the RPE. Furthermore, a novel method is proposed to segment the choroid-sclera interface (CSI), which employs the wavelet based features to construct a Gaussian mixture model (GMM). The model is then used in a graph cut for segmentation of the choroidal boundary. The proposed algorithm is tested on 100 EDI OCTs and is compared with manual segmentation. The results showed an unsigned error of 2.48 ± 0.32 pixels for BM extraction and 9.79 ± 3.29 pixels for choroid detection. It implies significant improvement of the proposed method over other approaches like k-means and graph cut methods. [ABSTRACT FROM AUTHOR]

This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices. [ABSTRACT FROM PUBLISHER]

Background: Early diagnosis of diseases is an important step in their treatment process, but it is difficult due to lack of satisfactorily sensitive imaging devices and difference in the parameters of the subjects. Asymmetry analysis of retinal nerve fiber layer thickness and total retinal thickness can provide a criterion for physicians to accomplish early diagnosis. Methods: Data were collected from 50 normal subjects (aged 35 ± 10 years) by a Topcon model of 3D-OCT 1000. For this purpose, a pipeline of procedures was utilized; segmentation of retinal layers by diffusion map method, scaling operator, automatic localization of the center of macula, and calculation of mean and SD. Findings: The maximum of average RNFL and total retina thickness in right and left eyes is seen in the perifoveal nasal, and the minimum is seen in the fovea. The results demonstrate an overall symmetry between the two eyes (P > 0.05). Tolerance limits in RNFL of normal subjects for 9 sectors (from 1 to 9), respectively, are 1.16 ± 0.99, 2.43 ± 1.57, 3.96 ± 2.08, 1.11 ± 1.09, 2.83 ± 1.66, 3.09 ± 1.39, 4.91 ± 1.79, 0.82 ± 1.25, and 3.31 ± 1.63 μm. Tolerance limits in total retina of normal subjects for 9 sectors, respectively, are 9.84 ± 2.75, 3.60 ± 2.74, 4.82 ± 3.23, 3.33 ± 1.76, 6.51 ± 3.69, 3.23 ± 1.30, 5.96 ± 2.62, 2.79 ± 1.42, 6.72 ± 2.57 μm. Conclusion: Asymmetry analysis can be a criterion for early detection of diseases. In this study asymmetry analysis and tolerance limits were calculated. [ABSTRACT FROM AUTHOR]

Background: Enhanced depth imaging optical coherence tomography images (EDI-OCT) is used for detailed imaging of the choroid layer that contains the highest amount of blood flow in the eye and is affected in several diseases such as choroidal polyps, age-related degeneration and central serous chorioretinopathy. Choroidal segmentation is really important, but the manual segmentation is time consuming and encounters difficulties when large numbers of data is available. Since a large amount of information is available in the images, non-automated and visual analysis of data is almost impossible for the ophthalmologist. The main goal of automatic segmentation was to help the ophthalmologists in the diagnosis and monitoring diseases related to the eye. Methods: The data used in this project was obtained from the Heidelberg OCT-HRA2-KT instrument. Fifty 2 dimensional data were used to evaluate the algorithm. In this study, the retinal pigment epithelium (RPE) and choroid was segmented using a boundary detection algorithm named dynamic programming. Findings: The proposed algorithm was compared with the manual segmentation and the results showed an unsigned error of 1.71 ± 0.93 pixels for retinal pigmented epithelium (RPE) extraction and 10.48 ± 4.11 pixels for choroid detection. It showed significant improvements over other approaches like k-means method. Conclusion: A few automated methods are applied in the choroid segmentation and most of the studies were mainly focused on the manual separation. In this study, a fast and automated method was provided for the segmentation of choroid area. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm as an enhancement and preprocessing step for acquired optical coherence tomography (OCT) images of the retina. The proposed method is composed of two steps, first of which is a denoising algorithm with wavelet diffusion based on a circular symmetric Laplacian model, and the second part can be described in terms of graph-based geometry detection and curvature correction according to the hyper-reflective complex layer in the retina. The proposed denoising algorithm showed an improvement of contrast-to-noise ratio from 0.89 to 1.49 and an increase of signal-to-noise ratio (OCT image SNR) from 18.27 to 30.43 dB. By applying the proposed method for estimation of the interpolated curve using a full automatic method, the mean ± SD unsigned border positioning error was calculated for normal and abnormal cases. The error values of 2.19 ± 1.25 and 8.53 ± 3.76 μm were detected for 200 randomly selected slices without pathological curvature and 50 randomly selected slices with pathological curvature, respectively. The important aspect of this algorithm is its ability in detection of curvature in strongly pathological images that surpasses previously introduced methods; the method is also fast, compared to the relatively low speed of similar methods. [ABSTRACT FROM AUTHOR]

• A cross-sectional population-based study in Isfahan, Iran • OCT in HCs, MS with and without ON, and NMOSD with and without ON • No significant difference to distinguish NMOSD and RRMS patients • All quadrants of pRNFL in the RRMS and NMOSD groups were thinner than HCs • GCL and total macula were significantly less than HC in all NMOSD and RRMS eyes : The aim of this study was to identify and compare the characteristics of retinal nerve layers using spectral domain-optical coherence tomography (SD-OCT) in neuromyelitis optica spectrum disorder (NMOSD), relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HCs). : It is a cross-sectional population-based study in Isfahan, Iran. We enrolled 98 participants including 45 NMOSD patients (90 eyes), 35 RRMS patients (70 eyes) and 18 HCs (36 eyes). Evaluation criteria were thickness of different sectors in peripapillary retinal nerve fiber layer (pRNFL) and intra-retinal layers around the macula. History of previous optic neuritis (ON) was obtained through chart review and medical record. : Without considering ON, total macular, ganglion cell layer (GCL) and pRNFL were significantly thinner in both groups of patients compared to HCs. On macular examination, GCL and total macular thickness were significantly thinner than HCs in all NMOSD and RRMS eyes with and without history of ON. While there was no significant difference between MS-ON and MS without a history of ON in the macular measures, the reduction in total macular and GCL thickness was significantly greater in NMOSD-ON compared to NMOSD without a history of ON. Also in NMOSD-ON eyes, the RNFL, GCL, IPL and GCIPL layers were significantly thinner than that of MS-ON. On the other hand, the pRNFL study showed significant thinning of all quadrants in the RRMS and NMOSD groups relative to HCs. While the decrease of pRNFL thickness in the eyes of NMOSD-ON and MS with and without a previous history of ON was significantly greater than that of HCs, no difference was observed between NMOSD without ON and HCs. In addition, in NMOSD patients, pRNFL was significantly thinner in eyes with history of ON compared to non ON-eyes. Furthermore, in patients with a history of ON, reduction in all sectors of pRNFl (except in T) was significantly greater in NMOSD compared to MS patients. : Our findings showed that although macular and retinal damage occurred in both NMOSD and RRMS patients without significant differences, the severity of injury in eyes with history of ON was significantly higher in NMOSD compared to MS patients, that could be considered as a marker to distinguish them. In addition, our results confirmed the absence of subclinical optic nerve involvement in NMOSD unlike MS patients. [ABSTRACT FROM AUTHOR]

Purpose: Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus images, commonly combined with OCT for fixed B-scan positions. However, no machine learning research has utilized IR-SLO images for automated MS diagnosis., Methods: This study utilized a dataset comprised of IR-SLO images and OCT data from Isfahan, Iran, encompassing 32 MS and 70 healthy individuals. A number of convolutional neural networks (CNNs)-namely, VGG-16, VGG-19, ResNet-50, ResNet-101, and a custom architecture-were trained with both IR-SLO images and OCT thickness maps as two separate input datasets. The highest performing models for each modality were then integrated to create a bimodal model that receives the combination of OCT thickness maps and IR-SLO images. Subject-wise data splitting was employed to prevent data leakage among training, validation, and testing sets., Results: Overall, images of the 102 patients from the internal dataset were divided into test, validation, and training subsets. Subsequently, we employed a bootstrapping approach on the training data through iterative sampling with replacement. The performance of the proposed bimodal model was evaluated on the internal test dataset, demonstrating an accuracy of 92.40% ± 4.1% (95% confidence interval [CI], 83.61-98.08), sensitivity of 95.43% ± 5.75% (95% CI, 83.71-100.0), specificity of 92.82% ± 3.72% (95% CI, 81.15-96.77), area under the receiver operating characteristic (AUROC) curve of 96.99% ± 2.99% (95% CI, 86.11-99.78), and area under the precision-recall curve (AUPRC) of 97.27% ± 2.94% (95% CI, 86.83-99.83). Furthermore, to assess the model generalization ability, we examined its performance on an external test dataset following the same bootstrap methodology, achieving promising results, with accuracy of 85.43% ± 0.08% (95% CI, 71.43-100.0), sensitivity of 97.33% ± 0.06% (95% CI, 83.33-100.0), specificity of 84.6% ± 0.10% (95% CI, 71.43-100.0), AUROC curve of 99.67% ± 0.02% (95% CI, 95.63-100.0), and AUPRC of 99.65% ± 0.02% (95% CI, 94.90-100.0)., Conclusions: Incorporating both modalities improves the performance of automated diagnosis of MS, showcasing the potential of utilizing IR-SLO as a complementary tool alongside OCT., Translational Relevance: Should the results of our proposed bimodal model be validated in future work with larger and more diverse datasets, diagnosis of MS based on both OCT and IR-SLO can be reliably integrated into routine clinical practice.

Optical Coherence Tomography (OCT) is one of the well-known imaging systems in ophthalmology that provides images with high resolution from retinal tissue. However, like other coherent imaging systems, OCT images suffer from speckle noise which decreases the image quality. Denoising can be considered as an estimation problem in a Bayesian framework. So, finding a suitable distribution for noiseless data is an important issue. We propose a statistical model for OCT data, namely Asymmetric Normal Laplace Mixture Model (ANLMM), and then convert its distribution to normal by Gaussianization Transform (GT). Finally, by applying the Spatially Constrained Gaussian Mixture Model (SC-GMM), a new OCT denoising algorithm is introduced, which significantly outperforms the other methods in terms of Contrast-to-Noise Ratio (CNR).

Optical Coherence Tomography (OCT) is known as a non-invasive and high resolution imaging modality in ophthalmology. Effecting noise on the OCT images as well as other reasons cause a random behavior in these images. In this study, we introduce a new statistical model for retinal layers in healthy OCT images. This model, namely asymmetric Normal Laplace (NL), fits well the advent of asymmetry and heavy-tailed in intensity distribution of each layer. Due to the layered structure of retina, a mixture model is addressed. It is proposed to evaluate the fitness criteria called Kull-back Leibler Divergence (KLD) and chi-square test along visual results. The results express the well performance of proposed model in fitness of data except for 6 th and 7 th layers. Using a complicated model, e.g. a mixture model with two component, seems to be appropriate for these layers. The mentioned process for train images can then be devised for a test image by employing the Expectation Maximization (EM) algorithm to estimate the values of parameters in mixture model.

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging.

In this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. We selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle-related modulus. The Laplacian mixture model, Rayleigh mixture model, and circular symmetric Laplacian mixture models were evaluated and, as it could be expected, the latter provided a better model because of its compatibility with heavy tailed structure of the wavelet coefficients besides their interscale dependence. The numerical evaluation of contrast-to-noise ratio (CNR) along with simple observation of the results showed reasonably acceptable improvement of CNR from 2.9149 to 38.8813 in anterior--posterior images, from 41.6131 to 86.3141 in cephal-lateral images, from 13.6414 to 43.4711 in intraoral pictures, and from 6.0102 to 31.8771 in panoramic datasets. Furthermore, technical ability of the proposed filtering method in retaining the possible cavities on dental images was evaluated in two datasets with natural and artificially applied cavities.