Normal Optical Coherence Tomography Images Research Articles

Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.

Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch's Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) , BCNN (VGG19) , and BCNN (Inception_V3) , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

Linear multifractional stable motion for modeling of fluid-filled regions in retinal optical coherence tomography images

Fluid appears in several retinal disorders can be visualized by optical coherence tomography (OCT) images. Since manual survey of OCT images is challenging, automatic fluid detection is desirable. This paper develops a generalized multifractal framework to model the local scale-invariant property of retinal OCT signals and accordingly proposes a simple methodology for automatic localization of fluid-filled regions. The proposed framework comprises both local self-similarity and heavy tailed distribution of OCT signals by generalization of fractional Levy stable motion (fLsm) to the Riemann-Liouville multifractional Levy stable motion (RL-mLsm). In order to measure the multifractality appears in RL-mLsm, the scaling properties of Levy flights and stochastic differential equations (SDEs) are used to develop a fractional momentbased algorithm namely, multi-fractional Levy fluctuation analysis (MLFA) algorithm. Here, we apply MLFA to model OCT images. Modeling results indicate the significant difference between the multifractal bandwidth in non-fluid and fluid regions. In addition, by utilizing various surrogate series of the signals, we discover the source of multifractality of OCT signals. As an application of the proposed multifractal modeling, we propose a new method, based on simple clustering of estimated local scaling exponent of modeled OCT signals, to localize fluid regions in OCT images. The localization method is validated, and the experimental results reveal the superiority of proposed method. Overall, the proposed multifractal framework has full advantages on excellent improvement of fluid localization, which promises the proposed model is suitable for effective description of self-similar nature of normal and abnormal OCT images independent of capturing device.

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.

A Dual-Discriminator Fourier Acquisitive GAN for Generating Retinal Optical Coherence Tomography Images

Optical coherence tomography (OCT) images are widely used for clinical examination of the retina. Automatic deep learning-based methods have been developed to classify normal and pathological OCT images. However, lack of the big enough training data reduces the performance of these models. Synthesis of data using generative adversarial networks (GANs) is already known as an efficient alternative to increase the amount of the training data. However, the recent works show that despite high structural similarity between synthetic data and the real images, a considerable distortion is observed in frequency domain. Here, we propose a dual discriminator Fourier acquisitive GAN (DDFA-GAN) to generate more realistic OCT images with considering the Fourier domain similarity in structural design of the GAN. By applying two discriminators, the proposed DDFA-GAN is jointly trained with the Fourier and spatial details of the images and is proven to be feasible with a limited number of training data. Results are compared with popular GANs, namely, DCGAN, WGAN-GP, and LS-GAN. In comparison, Fréchet inception distance (FID) score of 51.30, and Multi Scale Structural Similarity Index Measure (MS-SSIM) of 0.19 indicate superiority of the proposed method in producing images resembling the same quality, discriminative features, and diversity, as the real normal and Diabetic Macular Edema (DME) OCT images. The statistical comparison illustrates this similarity in the spatial and frequency domains, as well. Overall, DDFA-GAN generates realistic OCT images to meet requirements of the training data in automatic deep learning-based methods, used for clinical examination of the retina, and to improve the accuracy of the subsequent measurements.

Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images

In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In this paper, a hybrid Convolutional Neural Networks-based CAD system, which can classify Diabetic Macular Edema (DME), Drusen Choroidal Neovascularization (CNV), and normal OCT images, is proposed. The proposed system is CNN-SVM (Convolutional Neural Networks – Support Vector Machine) model and doesn’t require any additional extraction of feature or noise filtering on OCT images. A total of 968 OCT images is classified in pre-trained CNN methods with Alexnet, Resnet18 and Googlenet. Accuracy is achieved with highest Googlenet 97.4%. To examine the performance of the proposed CAD system, the CNN-SVM method achieves 98.96% with the highest accuracy hybrid Alexnet-SVM model, which is implemented with Alexnet-SVM, Resnet18-SVM and Googlenet-SVM models.

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic images helped in the extraction of the characteristic features of each rare disease. The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical network. By increasing the accuracy of diagnosing rare retinal diseases through FSL, clinicians can avoid neglecting rare diseases with DL assistance, thereby reducing diagnosis delay and patient burden.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11517-021-02321-1.

Machine learning classification of normal vs. age-related macular degeneration OCT images

The macula is a small area of the retina which is especially very important for good eyesight. The age- related macular degeneration (AMD) is a type of visual impairment that can cause blindness or even loss of eyesight. AMD was a dangerous and progressing chronic disease that affects people over the age of 60 years. One of the most common symptoms of this condition is the appearance of a type of extracellular material called the drusen. Detecting this condition using an imaging technique known as optical coherence tomography (OCT) can help to prevent further damage to the eyes. The motivation behind this work is to test machine learning (ML) with OCT images for the identification of retinal disease. OCT retinal images consist of normal retina and AMD. ML algorithms were used to classify 261 OCT images to determine if the person was normal or macular. The proposed method for diagnosing between diseased and healthy conditions has a classification accuracy that considerably exceeds beyond the current state of the art. This work will be used in developing further ML concepts in the diagnosis of ocular disorders and diseases.

Machine learning classification of normal vs. age-relatedmacular degeneration OCT images

The macula is a small area of the retina which is especially very important for good eyesight. The age- relatedmacular degeneration (AMD) is a type of visual impairment that can cause blindness or even loss of eyesight.AMD was a dangerous and progressing chronic disease that affects people over the age of 60 years. One of themost common symptoms of this condition is the appearance of a type of extracellular material called the drusen.Detecting this condition using an imaging technique known as optical coherence tomography (OCT) can help toprevent further damage to the eyes. The motivation behind this work is to test machine learning (ML) with OCTimages for the identification of retinal disease. OCT retinal images consist of normal retina and AMD. ML algorithmswere used to classify 261 OCT images to determine if the person was normal or macular. The proposed methodfor diagnosing between diseased and healthy conditions has a classification accuracy that considerably exceedsbeyond the current state of the art. This work will be used in developing further ML concepts in the diagnosis ofocular disorders and diseases.

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images

Background and ObjectiveRetinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina. MethodsIn the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm. ResultsTraining and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases. ConclusionIn the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina’s actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.

Imaging depth extension of optical coherence tomography in rabbit eyes using optical clearing agents.

Optical coherence tomography has become an indispensable diagnostic tool in ophthalmology for imaging the retina and the anterior segment of the eye. However, the imaging depth of optical coherence tomography is limited by light attenuation in tissues due to optical scattering and absorption. In this study of rabbit eye both ex vivo and in vivo, optical coherence tomography imaging depth of the anterior and posterior segments of the eye was extended by using optical clearing agents to reduce multiple scattering. The sclera, the iris, and the ciliary body were clearly visualized by direct application of glycerol at an incision on the conjunctiva, and the posterior boundary of sclera and even the deeper tissues were detected by submerging the posterior segment of eye in glycerol solution ex vivo or by retro-bulbar injection of glycerol in vivo. The ex vivo rabbit eyes recovered to their original state in 60 s after saline-wash treatment, and normal optical coherence tomography images of the posterior segment of the sample eyes proved the self-recovery of in vivo performance. Signal intensities of optical coherence tomography images obtained before and after glycerol treatment were compared to analysis of the effect of optical clearing. To the best of our knowledge, this is the first study for imaging depth extension of optical coherence tomography in both the anterior and posterior segments of eye by using optical clearing agents.

HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.

Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning based classification is proposed. The two important issues intended in this approach are avoiding retinal layer segmentation and attempting to mimic the authors' understanding based on normal and abnormal region identifications, considering that the signs of diseases appear in a small fraction of B-Scans. The histogram of oriented gradients feature descriptor was utilized to characterize the distribution of local intensity gradients and edge directions. To capture the structure of extracted features, different dictionary learning-based classifiers are employed. The dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME, and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13, 100.00, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering 4% of all B-Scans of a volume, which outperforms the state-of-the-art methods.

Retinal optical coherence tomography image classification with label smoothing generative adversarial network

In this paper, we propose a label smoothing generative adversarial network (LSGAN) for optical coherence tomography (OCT) image classification to identify drusen, i.e., the early stage of age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME) and normal OCT images. The LSGAN can expand the dataset to address the issue of overfitting when only limited OCT training samples are available. Specifically, our LSGAN consists of three components: generator, discriminator, and classifier. The generator generates synthetic unlabeled images that are similar to the real OCT images, while the discriminator distinguishes whether the synthetic images are real or generated. To train the classifier with both real labeled images and synthetic unlabeled images, we design artificial pseudo labels as label smoothing for the synthetic unlabeled images. Thus, the mixing of the synthetic images and real images can be used as training data to improve the classification performance. Experimental results on two real OCT datasets demonstrate the superiority of our LSGAN method over several well-known classifiers, especially under the condition of limited training data.

OCTID: Optical coherence tomography image database

Optical coherence tomography (OCT) is a non-invasive imaging modality that is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 high-resolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster scan protocol with a 2 mm scan length and 512 × 1024 pixel resolution. We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation. In addition, we have provided a user friendly GUI which can be used by clinicians for manual (and semi-automated) segmentation.

Optical Coherence Tomography-Based Deep-Learning Models for Classifying Normal and Age-Related Macular Degeneration and Exudative and Non-Exudative Age-Related Macular Degeneration Changes.

IntroductionThe use of optical coherence tomography (OCT) images is increasing in the medical treatment of age-related macular degeneration (AMD), and thus, the amount of data requiring analysis is increasing. Advances in machine-learning techniques may facilitate processing of large amounts of medical image data. Among deep-learning methods, convolution neural networks (CNNs) show superior image recognition ability. This study aimed to build deep-learning models that could distinguish AMD from healthy OCT scans and to distinguish AMD with and without exudative changes without using a segmentation algorithm.MethodsThis was a cross-sectional observational clinical study. A total of 1621 spectral domain (SD)-OCT images of patients with AMD and a healthy control group were studied. The first CNN model was trained and validated using 1382 AMD images and 239 normal images. The second transfer-learning model was trained and validated with 721 AMD images with exudative changes and 661 AMD images without any exudate. The attention area of the CNN was described as a heat map by class activation mapping (CAM). In the second model, which classified images into AMD with or without exudative changes, we compared the learning stabilization of models using or not using transfer learning.ResultsUsing the first CNN model, we could classify AMD and normal OCT images with 100% sensitivity, 91.8% specificity, and 99.0% accuracy. In the second, transfer-learning model, we could classify AMD as having or not having exudative changes, with 98.4% sensitivity, 88.3% specificity, and 93.9% accuracy. CAM successfully described the heat-map area on the OCT images. Including the transfer-learning model in the second model resulted in faster stabilization than when the transfer-learning model was not included.ConclusionTwo computational deep-learning models were developed and evaluated here; both models showed good performance. Automation of the interpretation process by using deep-learning models can save time and improve efficiency.Trial RegistrationNo15073.

Deep Learning Classification Models Built with Two-step Transfer Learning for Age Related Macular Degeneration Diagnosis.

The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.

Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images.

.In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed.

Shortest path with backtracking based automatic layer segmentation in pathological retinal optical coherence tomography images

Optical coherence tomography (OCT) is a high-resolution and non-invasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is crucial for doctors to diagnose and analysis retinal diseases. Manual segmentation is often a time-consuming and subjective process. A number of semi-automatic and automatic methods have been proposed for layer segmentation on retinal OCT images, but very few of them are applicable for retinal pathological OCT images. In this work, we propose a new automatic method for segmenting ILM (Inner Limiting Membrane) and OS-RPE (Outer Segment- Retinal Pigment Epithelium) interfaces on pathological OCT images affected by macular hole disease. The proposed method follows shortest path framework, while is enhanced with backtracking and direction consistency. Backtracking can deal with the shortcut problem which occur when classical shortest path algorithm is used to segment deformed linear structure. “Consistency loss” is one kind of soft constraint we defined to limit the propagate direction of modified shortest path algorithm. Besides, the image information after Gabor transform can reflect the layer location to an extent. So, it is considered as one new weight in the weight calculation of the method. Another contribution is that the proposed layer segmentation method is suitable for both normal and pathological retinal OCT images. To quantitate the performance of the proposed method, we did comparative experiments with three state-of-the-art segmentation methods. The experimental result shows that proposed method can achieve better result than other methods and can deal with pathological retinal OCT images.

Three-dimensional graph-based skin layer segmentation in optical coherence tomography images for roughness estimation.

Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.