1. Articles from Hossein Rabbani

    1-24 of 24
    1. Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

      Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

      Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. The LACNN simulates the ophthalmologists’ diagnosis that focuses on local lesion-related regions when analyzing the OCT image. Specifically, we ...

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    2. Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging

      Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging

      Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals of eye researchers. Material and Methods: The present study is designed in order to present a comparative study on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional ...

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      Mentions: Bioptigen
    3. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks

      Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks

      In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, the optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of ...

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    4. Three-dimensional Optical Coherence Tomography Image Denoising via Multi-input Fully-Convolutional Networks

      Three-dimensional Optical Coherence Tomography Image Denoising via Multi-input Fully-Convolutional Networks

      — In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Optical coherence tomography (OCT) images are inevitably affected by noise, due to the coherent nature of the image formation process. In this paper, we take advantage of the progress in deep learning methods and propose a new method termed multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. Despite recently proposed natural image denoising CNNs, our proposed architecture allows exploiting high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion ...

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    5. Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble

      Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble

      Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a ...

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    6. Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

      Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

      We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each ...

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    7. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

      Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

      The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes ...

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    8. Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithm

      Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithm

      This paper presents a fully-automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of the subjects with diabetic macular edema (DME). The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image g is transformed into three sets: T (true), I (indeterminate) that represents noise, and F (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set I, and a new λ-correction operation is introduced to compute the set T in neutrosophic domain. Second, a ...

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    9. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

      Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

      Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following ...

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    10. Speckle Noise Reduction in Optical Coherence Tomography Using 2D Curvelet Based Dictionary Learning

      Speckle Noise Reduction in Optical Coherence Tomography Using 2D Curvelet Based Dictionary Learning

      The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. Then, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary to be dependent on the ...

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    11. Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD

      Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD

      This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinalcysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherencetomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, andshow the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximatethe corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce anew scheme in dictionary learning and take curvelet transform of filtered image ...

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    12. Statistical Modeling of Retinal Optical Coherence Tomography

      Statistical Modeling of Retinal Optical Coherence Tomography

      In this paper, a new model for retinal Optical Coherent Tomography (OCT) images is proposed. This statistical model is based on introducing a nonlinear transform, namely Gaussianization, to convert the probability distribution function (pdf) of each OCT intra-retinal layer to a Gaussian distribution. According to retina anatomy, the retina is a layered structure and in OCT images each of these layers has a specific pdf which is corrupted by speckle noise, therefore a mixture model for statistical modeling of OCT images is proposed. A Normal-Laplace distribution, which is a convolution of a Laplace pdf and Gaussian noise, is proposed as ...

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    13. Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography

      Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography

      In this paper, we discuss about applications of different methods for decomposing a signal over elementary waveforms chosen in a family called a dictionary (atomic representations) in optical coherence tomography (OCT). If the representation is learned from the data, a nonparametric dictionary is defined with three fundamental properties of being data-driven, applicability on 3D, and working in multi-scale, which make it appropriate for processing of OCT images. We discuss about application of such representations including complex wavelet based K-SVD, and diffusion wavelets on OCT data. We introduce complex wavelet based K-SVD to take advantage of adaptability in dictionary learning methods ...

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    14. An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model

      An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model

      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 ...

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    15. Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts

      Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts

      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 ...

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    16. Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral Domain Optical Coherence Tomography

      Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral Domain Optical Coherence Tomography

      Purpose. 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. Methods. Mean regional retinal thickness of 11 retinal layers were obtained by automatic three-dimensional diffusion-map-based method in 112 normal eyes of 76 Iranian subjects. Results. The thickness map of central foveal area in layer 1, 3, and 4 displayed the minimum thickness (P<0.005 for all). Maximum thickness was observed in nasal to the fovea of layer 1 (P<0.001) and in a circular pattern in ...

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

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

      In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images ...

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    18. Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model

      Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model

      The new techniques of three-dimensional (3D)-optical coherence tomography (OCT) imaging is very useful for detecting retinal pathologic changes in various diseases and determining retinal thickness ‘abnormalities’. Fundus colour images have been used for several years for detecting retinal abnormalities too. If the two image modalities were combined, the resulted image would be more informative. The first step to combine these two modalities is to register colour fundus images with an en face representation of OCT. In this study, curvelet transform is used to extract vessels for both modalities. Then the extracted vessels from two modalities are registered together in ...

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    19. Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary

      Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary

      Optical Coherence Tomography (OCT) suffers from speckle noise which causes erroneous interpretation. OCT denoising methods may be studied in "raw image domain" and "sparse representation". Comparison of mentioned denoising strategies in magnitude domain shows that wavelet-thresholding methods had the highest ability, among which wavelets with shift invariant property yielded better results. We chose dictionary learning to improve the performance of available wavelet-thresholding by tailoring adjusted dictionaries instead of using pre-defined bases. Furthermore, in order to take advantage of shift invariant wavelets, we introduce a new scheme in dictionary learning which starts from a dual tree complex wavelet. we investigate the ...

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    20. A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

      A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

      Optical coherence tomography (OCT) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content [1]. In contrast to OCT technology development which has been a field of active research since 1991, OCT image segmentation has only been more fully explored during the last decade. Segmentation, however, remains one of the most difficult and at the same time most commonly required steps in OCT image analysis. No typical segmentation method exists that can be expected to work equally well for all tasks [2 ...

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    21. An Accurate Multimodal 3D Vessel Segmentation Method Based on Brightness Variations on OCT Layers and Curvelet Domain Fundus Image Analysis

      An Accurate Multimodal 3D Vessel Segmentation Method Based on Brightness Variations on OCT Layers and Curvelet Domain Fundus Image Analysis

      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 2D 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 (ONH). The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that Retinal Nerve Fiber Layer (RNFL) becomes thicker in presence of the blood vessels . The second stage ...

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    22. Curvature correction of retinal OCTs using graph-based geometry detection

      Curvature correction of retinal OCTs using graph-based geometry detection

      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 ...

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    23. Detection and registration of vessels of fundus and OCT images using curevelet analysis

      Detection and registration of vessels of fundus and OCT images using curevelet analysis

      In recent years, advanced analysis of retinal images, has built automatic systems for diagnosis of various diseases. These devices help us save both time and money. The new techniques of 3D-Optical Coherence Tomography (OCT) imaging is very useful for detecting retinal pathologic changes in various diseases and determining retinal thickness abnormalities. Fundus color images have been used for several years for detecting retinal abnormalities too. If the two image modalities were combined, the resulted image would be more informative because some abnormalities such as drusen, geographic atrophy, and macular hemorrhages are detected in color fundus images but the exact morphology ...

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    24. Intra-Retinal Layer Segmentation of 3D Optical Coherence Tomography Using Coarse Grained Diffusion Map

      Intra-Retinal Layer Segmentation of 3D Optical  Coherence Tomography Using Coarse Grained  Diffusion Map
      Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on s pectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping is applied to 2D and 3D OCT datasets composed of two steps, one for ...
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    1-24 of 24
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    Intra-Retinal Layer Segmentation of 3D Optical  Coherence Tomography Using Coarse Grained  Diffusion Map Detection and registration of vessels of fundus and OCT images using curevelet analysis Curvature correction of retinal OCTs using graph-based geometry detection An Accurate Multimodal 3D Vessel Segmentation Method Based on Brightness Variations on OCT Layers and Curvelet Domain Fundus Image Analysis A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral Domain Optical Coherence Tomography An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model Optimal intereye difference thresholds by optical coherence tomography in multiple sclerosis: An international study Visualizing choriocapillaris using swept-source optical coherence tomography angiography with various probe beam sizes