Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution.

This paper suggests a novel hybrid tensor-ring (TR) decomposition and first-order tensor-based total variation (FOTTV) model, known as the TRFOTTV model, for super-resolution and noise suppression of optical coherence tomography (OCT) images. OCT imaging faces two fundamental problems undermining correct OCT-based diagnosis: significant noise levels and low sampling rates to speed up the capturing process. Inspired by the effectiveness of TR decomposition in analyzing complicated data structures, we suggest the TRFOTTV model for noise suppression and super-resolution of OCT images. Initially, we extract the nonlocal 3D patches from OCT data and group them to create a third-order low-rank tensor. Subsequently, using TR decomposition, we extract the correlations among all modes of the grouped OCT tensor. Finally, FOTTV is integrated into the TR model to enhance spatial smoothness in OCT images and conserve layer structures more effectively. The proximal alternating minimization and alternative direction method of multipliers are applied to solve the obtained optimization problem. The effectiveness of the suggested method is verified by four OCT datasets, demonstrating superior visual and numerical outcomes compared to state-of-the-art procedures.

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.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

X-Let's Atom Combinations for Modeling and Denoising of OCT Images by Modified Morphological Component Analysis.

An improved analysis of Optical Coherence Tomography (OCT) images of the retina is of essential importance for the correct diagnosis of retinal abnormalities. Unfortunately, OCT images suffer from noise arising from different sources. In particular, speckle noise caused by the scattering of light waves strongly degrades the quality of OCT image acquisitions. In this paper, we employ a Modified Morphological Component Analysis (MMCA) to provide a new method that separates the image into components that contain different features as texture, piecewise smooth parts, and singularities along curves. Each image component is computed as a sparse representation in a suitable dictionary. To create these dictionaries, we use non-data-adaptive multi-scale ( X -let) transforms which have been shown to be well suitable to extract the special OCT image features. In this way, we reach two goals at once. On the one hand, we achieve strongly improved denoising results by applying adaptive local thresholding techniques separately to each image component. The denoising performance outperforms other state-of-the-art denoising algorithms regarding the PSNR as well as no-reference image quality assessments. On the other hand, we obtain a decomposition of the OCT images in well-interpretable image components that can be exploited for further image processing tasks, such as classification.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification.

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time-frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time-frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.

Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.

Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases.

Background: Optical coherence tomography (OCT) imaging significantly contributes to ophthalmology in the diagnosis of retinal disorders such as age-related macular degeneration and diabetic macular edema. Both diseases involve the abnormal accumulation of fluids, location, and volume, which is vitally informative in detecting the severity of the diseases. Automated and accurate fluid segmentation in OCT images could potentially improve the current clinical diagnosis. This becomes more important by considering the limitations of manual fluid segmentation as a time-consuming and subjective to error method. Methods: Deep learning techniques have been applied to various image processing tasks, and their performance has already been explored in the segmentation of fluids in OCTs. This article suggests a novel automated deep learning method utilizing the U-Net structure as the basis. The modifications consist of the application of transformers in the encoder path of the U-Net with the purpose of more concentrated feature extraction. Furthermore, a custom loss function is empirically tailored to efficiently incorporate proper loss functions to deal with the imbalance and noisy images. A weighted combination of Dice loss, focal Tversky loss, and weighted binary cross-entropy is employed. Results: Different metrics are calculated. The results show high accuracy (Dice coefficient of 95.52) and robustness of the proposed method in comparison to different methods after adding extra noise to the images (Dice coefficient of 92.79). Conclusions: The segmentation of fluid regions in retinal OCT images is critical because it assists clinicians in diagnosing macular edema and executing therapeutic operations more quickly. This study suggests a deep learning framework and novel loss function for automated fluid segmentation of retinal OCT images with excellent accuracy and rapid convergence result.

Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, mainly due to slice-wise splits of the data, creating dependency between training and test sets due to shared data across the sets. We introduce a new dataset of CT images (ISFCT Dataset) with labels indicating the subject-wise split to train and test our DL algorithms in an unbiased manner. We also use this dataset to validate the real performance of the published works in a subject-wise data split. Another key feature provides more specific labels (eight characteristic lung features) rather than being limited to COVID-19 and healthy labels. We show that the reported high accuracy of the existing models on current slice-wise splits is not repeatable for subject-wise splits, and distribution differences between data splits are demonstrated using t-distribution stochastic neighbor embedding. We indicate that, by examining subject-wise data splitting, less complicated models show competitive results compared to the exiting complicated models, demonstrating that complex models do not necessarily generate accurate and repeatable results.

Discrimination of multiple sclerosis using OCT images from two different centers.

BACKGROUND: Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as a noninvasive biomarker for monitoring of MS. There are successful reports regarding the application of Artificial Intelligence (AI) in the analysis of cross-sectional OCTs in ophthalmologic diseases. However, the alteration of thicknesses of various retinal layers in MS is noticeably subtle compared to other ophthalmologic diseases. Therefore, raw cross-sectional OCTs are replaced with multilayer segmented OCTs for discrimination of MS and healthy controls (HCs). METHODS: To conform to the principles of trustworthy AI, interpretability is provided by visualizing the regional layer contribution to classification performance with the proposed occlusion sensitivity approach. The robustness of the classification is also guaranteed by showing the effectiveness of the algorithm while being tested on the new independent dataset. The most discriminative features from different topologies of the multilayer segmented OCTs are selected by the dimension reduction method. Support vector machine (SVM), random forest (RF), and artificial neural network (ANN) are used for classification. Patient-wise cross-validation (CV) is utilized to evaluate the performance of the algorithm, where the training and test folds contain records from different subjects. RESULTS: The most discriminative topology is determined to square with a size of 40 pixels and the most influential layers are the ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL). Linear SVM resulted in 88% Accuracy (with standard deviation (std) = 0.49 in 10 times of execution to indicate the repeatability), 78% precision (std=1.48), and 63% recall (std=1.35) in the discrimination of MS and HCs using macular multilayer segmented OCTs. CONCLUSION: The proposed classification algorithm is expected to help neurologists in the early diagnosis of MS. This paper distinguishes itself from other studies by employing two distinct datasets, which enhances the robustness of its findings in comparison with previous studies with lack of external validation. This study aims to circumvent the utilization of deep learning methods due to the limited quantity of the available data and convincingly demonstrates that favorable outcomes can be achieved without relying on deep learning techniques.

Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms.

The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). Optical Coherence Tomography (OCT) is a vital retinal imaging technology. X-lets (such as curvelet, DTCWT, contourlet, etc.) have several benefits in image processing and analysis. They can capture both local and non-local features of an image simultaneously. The aim of this paper is to propose an optimal deep learning architecture based on sparse basis functions for the automated segmentation of cystic areas in OCT images. Different X-let transforms were used to produce different network inputs, including curvelet, Dual-Tree Complex Wavelet Transform (DTCWT), circlet, and contourlet. Additionally, three different combinations of these transforms are suggested to achieve more accurate segmentation results. Various metrics, including Dice coefficient, sensitivity, false positive ratio, Jaccard index, and qualitative results, were evaluated to find the optimal networks and combinations of the X-let's sub-bands. The proposed network was tested on both original and noisy datasets. The results show the following facts: (1) contourlet achieves the optimal results between different combinations; (2) the five-channel decomposition using high-pass sub-bands of contourlet transform achieves the best performance; and (3) the five-channel decomposition using high-pass sub-bands formations out-performs the state-of-the-art methods, especially in the noisy dataset. The proposed method has the potential to improve the accuracy and speed of the segmentation process in clinical settings, facilitating the diagnosis and treatment of retinal diseases.

Detecting red-lesions from retinal fundus images using unique morphological features.

One of the most important retinal diseases is Diabetic Retinopathy (DR) which can lead to serious damage to vision if remains untreated. Red-lesions are from important demonstrations of DR helping its identification in early stages. The detection and verification of them is helpful in the evaluation of disease severity and progression. In this paper, a novel image processing method is proposed for extracting red-lesions from fundus images. The method works based on finding and extracting the unique morphological features of red-lesions. After quality improvement of images, a pixel-based verification is performed in the proposed method to find the ones which provide a significant intensity change in a curve-like neighborhood. In order to do so, a curve is considered around each pixel and the intensity changes around the curve boundary are considered. The pixels for which it is possible to find such curves in at least two directions are considered as parts of red-lesions. The simplicity of computations, the high accuracy of results, and no need to post-processing operations are the important characteristics of the proposed method endorsing its good performance.

Mixed multiscale BM4D for three-dimensional optical coherence tomography denoising.

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated.

Exudate identification in retinal fundus images using precise textural verifications.

One of the most salient diseases of retina is Diabetic Retinopathy (DR) which may lead to irreparable damages to eye vision in the advanced phases. A large number of the people infected with diabetes experience DR. The early identification of DR signs facilitates the treatment process and prevents from blindness. Hard Exudates (HE) are bright lesions appeared in retinal fundus images of DR patients. Thus, the detection of HEs is an important task preventing the progress of DR. However, the detection of HEs is a challenging process due to their different appearance features. In this paper, an automatic method for the identification of HEs with various sizes and shapes is proposed. The method works based on a pixel-wise approach. It considers several semi-circular regions around each pixel. For each semi-circular region, the intensity changes around several directions and non-necessarily equal radiuses are computed. All pixels for which several semi-circular regions include considerable intensity changes are considered as the pixels located in HEs. In order to reduce false positives, an optic disc localization method is proposed in the post-processing phase. The performance of the proposed method has been evaluated on DIARETDB0 and DIARETDB1 datasets. The experimental results confirm the improved performance of the suggested method in term of accuracy.

Cyst identification in retinal optical coherence tomography images using hidden Markov model.

Optical Coherence Tomography (OCT) is a useful imaging modality facilitating the capturing process from retinal layers. In the salient diseases of retina, cysts are formed in retinal layers. Therefore, the identification of cysts in the retinal layers is of great importance. In this paper, a new method is proposed for the rapid detection of cystic OCT B-scans. In the proposed method, a Hidden Markov Model (HMM) is used for mathematically modelling the existence of cyst. In fact, the existence of cyst in the image can be considered as a hidden state. Since the existence of cyst in an OCT B-scan depends on the existence of cyst in the previous B-scans, HMM is an appropriate tool for modelling this process. In the first phase, a number of features are extracted which are Harris, KAZE, HOG, SURF, FAST, Min-Eigen and feature extracted by deep AlexNet. It is shown that the feature with the best discriminating power is the feature extracted by AlexNet. The features extracted in the first phase are used as observation vectors to estimate the HMM parameters. The evaluation results show the improved performance of HMM in terms of accuracy.

An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal. METHOD: In the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy. RESULTS: BCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.

Reflux definitions in esophageal multi-channel intraluminal impedance.

Aim: In this study, we aim to propose consensus-based interpretations to enhance both automatic, and manual analysis and then present our recommendations about reflux-related variables to enhance Multichannel Intraluminal (MII) measurements. Background: Multichannel Intraluminal Impedance-pH (MII-pH) monitoring is the most sensible option to evaluate Gastroesophageal Reflux Disease (GERD), specifically for the patients with normal endoscopy findings, and persistent symptoms without response to Proton Pomp Inhibitor therapy. There were only a few studies on the interpretation of reflux events in MII tracings. Methods: Several 200 episodes of reflux events were reviewed during several meetings in five steps, to discuss and categorize unresolved issues within existing interpretations, and propose technical principles for accurate characterization of reflux events. Results: In this study, we show that baseline impedance is determined using a moving average procedure to the impedance data of each channel with a time window of 60 seconds based on this finding; a liquid reflux event is defined as a retrograde 50% drop in baseline impedance, gas reflux event is defined as a rapid increase in impedance greater than 5 kΩ, Mixed liquid-gas reflux is defined as gas reflux occurring immediately before or during liquid reflux. Conclusion: The reliability of final diagnosis is significantly dependent on the accurate detection of reflux events, which is currently confronting technical limitations. A pathological reflux event propagates to at least three of the impedance sites, according to the literature. We think that taking three impedance locations into account might be too strict.

Retinal optical coherence tomography image analysis by a restricted Boltzmann machine.

Optical coherence tomography (OCT) is an emerging imaging technique for ophthalmic disease diagnosis. Two major problems in OCT image analysis are image enhancement and image segmentation. Deep learning methods have achieved excellent performance in image analysis. However, most of the deep learning-based image analysis models are supervised learning-based approaches and need a high volume of training data (e.g., reference clean images for image enhancement and accurate annotated images for segmentation). Moreover, acquiring reference clean images for OCT image enhancement and accurate annotation of the high volume of OCT images for segmentation is hard. So, it is difficult to extend these deep learning methods to the OCT image analysis. We propose an unsupervised learning-based approach for OCT image enhancement and abnormality segmentation, where the model can be trained without reference images. The image is reconstructed by Restricted Boltzmann Machine (RBM) by defining a target function and minimizing it. For OCT image enhancement, each image is independently learned by the RBM network and is eventually reconstructed. In the reconstruction phase, we use the ReLu function instead of the Sigmoid function. Reconstruction of images given by the RBM network leads to improved image contrast in comparison to other competitive methods in terms of contrast to noise ratio (CNR). For anomaly detection, hyper-reflective foci (HF) as one of the first signs in retinal OCTs of patients with diabetic macular edema (DME) are identified based on image reconstruction by RBM and post-processing by removing the HFs candidates outside the area between the first and the last retinal layers. Our anomaly detection method achieves a high ability to detect abnormalities.

Stochastic Differential Equations for Automatic Quality Control of Retinal Optical Coherence Tomography images.

Optical coherence tomography is widely used to provide high resolution images from retina. During data acquisition, several artifacts may be associated with OCT images which clearly remove information of retinal layers and degrade the quality of images. Manual assessment of the acquired OCT images is hard and time consuming. Therefore, an automatic quality control step is necessary to detect poor images for next decisions of eliminating them and even re-scanning. In this study, a novel automatic quality control methodology is proposed for early assessment of the OCT images quality by employing stochastic differential equations (SDE). In this method α-stable nature of OCT images is represented by applying a fractional Laplacian filter and parameters of the obtained α-stable are fed to an SVM to automatically detect high quality vs poor quality images. The simulation results on a large dataset of normal and abnormal OCT images show that proposed method has outstanding performance in detection of poor vs high quality images. The methodology is applicable to the image quality assessment of other OCT scanning devices as well. Clinical Relevance- Automatic quality control assessment of retinal OCT images provides reliable data for diagnosis of retinal and systematic diseases in clinical applications.

Automatic Classification of Macular Diseases from OCT Images Using CNN Guided with Edge Convolutional Layer.

Optical Coherence Tomography (OCT) is a non-invasive imaging technology that is widely applied for the diagnosis of retinal pathologies. In general, the structural information of retinal layers plays an important role in the diagnosis of various eye diseases by ophthalmologists. In this paper, by focusing on this information, we first introduce a new layer called the edge convolutional layer (ECL) to accurately extract the retinal boundaries in different sizes and angles with a much smaller number of parameters than the conventional convolutional layer. Then, using this layer, we propose the ECL-guided convolutional neural network (ECL-CNN) method for the automatic classification of the OCT images. For the assessment of the proposed method, we utilize a publicly available data comprising 45 OCT volumes with 15 age-related macular degeneration (AMD), 15 diabetic macular edema (DME), and 15 normal volumes, captured by using the Heidelberg OCT imaging device. Experimental results demonstrate that the suggested ECL-CNN approach has an outstanding performance in OCT image classification, which achieves an average precision of 99.43% as a three-class classification work. Clinical Relevance - The objective of this research is to introduce a new approach based on CNN for the automated classification of retinal OCT images. Clinically, the ophthalmologists should manually check each cross-sectional B-scan and classify retinal pathologies from B-scan images. This manual process is tedious and time-consuming in general. Hence, an automatic computer-assisted technique for retinal OCT image classification is demanded.

Mixture of Symmetric Stable Distributions for Macular Pathology Detection in Optical Coherence Tomography Scans.

Optical coherence tomography (OCT) is widely used to detect retinal disorders. In this study a new methodology is proposed for automatic detection of macular pathologies in the OCT images. Our approach is based on modeling the normal and abnormal OCT images with α-stable mixture model represented by stochastic differential equations (SDE). Parameters of the model are used to detect abnormal OCT images. The α-stable mixture model is created after applying a fractional Laplacian operator to the image and Expectation-Maximization (EM) algorithm is applied to estimate its parameters. The classification of an OCT image as normal or abnormal would be done by training SVM classifier based on estimated parameters of the mixture model. This method is examined for macular abnormality detection such as AMD, DME, and MH and achieve maximum accuracy of 97.8%. Clinical Relevance - This study establishes automatic method for anomaly detection on OCT images and provides fast and accurate OCT interpretation in clinical application.