1. Articles from Xinjian Chen

    1-24 of 29 1 2 »
    1. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images

      Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level. The proposed deep convolutional neural network (CNN) applies strategies including residual learning, shortcut connection, batch normalization and leaky rectified linear units to achieve good despeckling performance. Application of the proposed method to the OCT images shows great improvement in both visual quality and quantitative indices. The proposed method provides ...

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    2. Retinal Optical Coherence Tomography Image Analysis (Textbook)

      Retinal Optical Coherence Tomography Image Analysis (Textbook)

      This book introduces the latest optical coherence tomography (OCT) imaging and computerized automatic image analysis techniques, and their applications in the diagnosis and treatment of retinal diseases. Discussing the basic principles and the clinical applications of OCT imaging, OCT image preprocessing, as well as the automatic detection and quantitative analysis of retinal anatomy and pathology, it includes a wealth of clinical OCT images, and state-of-the-art research that applies novel image processing, pattern recognition and machine learning methods to real clinical data. It is a valuable resource for researchers in both medical image processing and ophthalmic imaging.

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    3. Effect Of Optic Disk—Fovea Distance On Measurements Of Individual Macular Intraretinal Layers In Normal Subjects

      Effect Of Optic Disk—Fovea Distance On Measurements Of Individual Macular Intraretinal Layers In Normal Subjects

      Purpose: To investigate the effect of optic disk—fovea distance (DFD) on measurements of macular intraretinal layers using spectral domain optical coherence tomography in normal subjects. Methods: One hundred and eighty-two eyes from 182 normal subjects were imaged using spectral domain optical coherence tomography. The average thicknesses of eight macular intraretinal layers were measured using an automatic segmentation algorithm. Partial correlation test and multiple regression analysis were used to determine the effect of DFD on thicknesses of intraretinal layers. Results: Disk—fovea distance correlated negatively with the overall average thickness in all the intraretinal layers (r ≤ −0.17, all P ...

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    4. Nondestructive Measurement of Conformal Coating Thickness on Printed Circuit Board with Ultra-high Resolution Optical Coherence Tomography

      Nondestructive Measurement of Conformal Coating Thickness on Printed Circuit Board with Ultra-high Resolution Optical Coherence Tomography

      Conformal coating (CC) is widely used to protect printed circuit board (PCB) from corrosion, mould growth and electrical failures. To ensure an effective protection, the thickness of the conformal coating layer needs to be well controlled. However, to date, the coating thickness is usually measured in a destructive way under microscopes. In this study, we proposed to use optical coherence tomography (OCT) to measure the CC thickness nondestructively. Specifically, to obtain a good accuracy in thickness measurement, we constructed a spectral domain OCT (SD-OCT) with ultra-high axial resolution to image the CC layer in three dimensions, and developed an image ...

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    5. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

      Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

      Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance ...

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    6. Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

      Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

      Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7 ...

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    7. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior

      Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior

      Automated segmentation of choroidal neovascularization (CNV) in optical coherence tomography (OCT) images plays an important role for the treatment of CNV disease. This paper proposes multi-scale convolutional neural networks with structure prior to segment CNV from OCT data. The proposed framework consists of two stages. In the first stage, the structure prior learning method based on sparse representation-based classification and the local potential function is developed to capture the global spatial structure and local similarity structure prior. The obtained prior can be used to improve the distinctiveness between CNV and background patches. In the second stage, multi-scale CNN model with ...

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    8. Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

      Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

      Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate ...

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    9. Quantitative analysis of retinal layers on three-dimensional spectral-domain optical coherence tomography for pituitary adenoma

      Quantitative analysis of retinal layers on three-dimensional spectral-domain optical coherence tomography for pituitary adenoma

      Purpose To quantitatively investigate the characteristics of eyes with pituitary adenoma presented by three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) using three common indices, including thickness, optical intensity ratio, and optical intensity attenuation coefficient (OIAC). Methods The SD-OCT database of 38 patients with pituitary adenoma and 39 normal controls were included in the study. Quadrantal and average measurements of thickness, optical intensity ratio, and OIAC were calculated for macular retinal nerve fiber layer (mRNFL), ganglion cell layer (GCL) combined with inner plexiform layer (IPL) (GCIPL) and/or the collective ganglion cell complex (GCC). The parameters of patients and controls were ...

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    10. Retinal SD-OCT image-based pituitary tumor screening

      Retinal SD-OCT image-based pituitary tumor screening

      In most cases, the pituitary tumor compresses optic chiasma and causes optic nerves atrophy, which will reflect in retina. In this paper, an Adaboost classification based method is first proposed to screen pituitary tumor from retinal spectral- domain optical coherence tomography (SD-OCT) image. The method includes four parts: pre-processing, feature extraction and selection, training and testing. First, in the pre-processing step, the retinal OCT image is segmented into 10 layers and the first 5 layers are extracted as our volume of interest (VOI). Second, 19 textural and spatial features are extracted from the VOI. Principal component analysis (PCA) is utilized ...

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    11. Nonrigid registration of 3D longitudinal optical coherence tomography volumes with choroidal neovascularization

      Nonrigid registration of 3D longitudinal optical coherence tomography volumes with choroidal neovascularization

      In this paper, we propose a 3D registration method for retinal optical coherence tomography (OCT) volumes. The proposed method consists of five main steps: First, a projection image of the 3D OCT scan is created. Second, the vessel enhancement filter is applied on the projection image to detect vessel shadow. Third, landmark points are extracted based on both vessel positions and layer information. Fourth, the coherent point drift method is used to align retinal OCT volumes. Finally, a nonrigid B-spline-based registration method is applied to find the optimal transform to match the data. We applied this registration method on 15 ...

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    12. Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

      Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

      In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segment retinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includes two parts: preprocessing and layer segmentation. During the preprocessing phase, images is first filtered for denoising, then the B-scans are flattened. During layer segmentation, the AAM is first used to obtain the coarse segmentation results. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficiently integrated into the graph search segmentation process. The proposed method was tested ...

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    13. Depth-encoded dual beam phase-resolved Doppler OCT for Doppler-angle-independent flow velocity measurement

      Depth-encoded dual beam phase-resolved Doppler OCT for Doppler-angle-independent flow velocity measurement

      Phase-resolved Doppler optical coherence tomography (PR-D-OCT) is a functional OCT imaging technique that can provide high-speed and high-resolution depth-resolved measurement on flow in biological materials. However, a common problem with conventional PR-D-OCT is that this technique often measures the flow motion projected onto the OCT beam path. In other words, it needs the projection angle to extract the absolute velocity from PR-D-OCT measurement. In this paper, we proposed a novel dual-beam PR-D-OCT method to measure absolute flow velocity without separate measurement on the projection angle. Two parallel light beams are created in sample arm and focused into the sample at ...

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    14. Dual-beam angular compounding for speckle reduction in optical coherence tomography

      Dual-beam angular compounding for speckle reduction in optical coherence tomography

      Optical coherence tomography (OCT), as a low-coherence interferometric imaging technique, inevitably suffers from speckle noise, which can reduce image quality and signal-to-noise (SNR). In this paper, we present a dual-beam angular compounding method to reduce speckle noise and improve SNR of OCT image. Two separated parallel light beams are created on the sample arm using a 1x2 optical fiber coupler and are focused into samples at different angles. The epi-detection scheme creates three different light path combinations of these two light beams above. The three combinations produce three images in single B-scan, which are completely separated in depth. The three ...

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    15. Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

      Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

      Speckle artifacts can strongly hamper quantitative analysis of optical coherence tomography (OCT), which is necessary to provide assessment of ocular disorders associated with vision loss. Here, we introduce a method for speckle reduction, which leverages from low-rank + sparsity decomposition (LRpSD) of the logarithm of intensity OCT images. In particular, we combine nonconvex regularization-based low-rank approximation of an original OCT image with a sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm, which is not an accurate rank indicator. As opposed to that, the proposed method provides ...

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    16. A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT

      A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT

      Branch retinal artery occlusion (BRAO) is an ocular emergency which could lead to blindness. Quantitative analysis of BRAO region in the retina is very needed to assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to classify and segment BRAO based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, a support vector machine (SVM) based classifier is designed to differentiate BRAO into acute phase and chronic phase, and the two types are segmented separately. To segment BRAO ...

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      Mentions: Haoyu Chen
    17. Depths-encoded angular compounding for speckle reduction in optical coherence tomography

      Depths-encoded angular compounding for speckle reduction in optical coherence tomography

      Optical coherence tomography (OCT) is one of the successful inventions in medical imaging as a clinic routine in the past decades. This imaging technique is based on low coherence interferometer and consequently suffers from speckle noise inherently, which can degrade image quality and obscure micro-structures. Therefore, effective speckle reduction techniques have been always desired and researched since optical coherence tomography was invented. In this study, we proposed an angular compounding method to reduce speckle noise of OCT image. Two different angular light paths are created on the sample arm using two beam splitters. The epi-detection scheme creates three different combinations ...

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    18. Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical ...

      Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical ...

      Purpose To investigate the profile and determinants of retinal optical intensity in normal subjects using 3D spectral domain optical coherence tomography (SD OCT). Methods A total of 231 eyes from 231 healthy subjects ranging in age from 18 to 80 years were included and underwent a 3D OCT scan. Forty-four eyes were randomly chosen to be scanned by two operators for reproducibility analysis. Distribution of optical intensity of each layer and regions specified by the Early Treatment of Diabetic Retinopathy Study (ETDRS) were investigated by analyzing the OCT raw data with our automatic graph-based algorithm. Univariate and multivariate analyses were ...

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    19. Automated segmentation of serous pigment epithelium detachment in SD-OCT images

      Automated segmentation of serous pigment epithelium detachment in SD-OCT images

      Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorio-retinal disease processes, which can cause the loss of central vision. A 3-D method is proposed to automatically segment serous PED in SD-OCT images. The proposed method consists of five steps: first, a curvature anisotropic diffusion filter is applied to remove speckle noise. Second, the graph search method is applied for abnormal retinal layer segmentation associated with retinal pigment epithelium (RPE) deformation. During this process, Bruch’s membrane, which doesn’t show in the SD-OCT images, is estimated with the convex hull algorithm. Third, the foreground and background seeds ...

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    20. Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion

      Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion

      Optical coherence tomography (OCT) provides not only morphological information but also information about layer-specific optical intensities, which may represent the underlying tissue properties. The purpose of this study is to quantitatively investigate the optical intensity of each retinal layers in central retinal artery occlusion (CRAO). Twenty-nine CRAO cases at acute phase and 33 normal controls were included. Macula-centered 3D OCT images were segmented with a fully-automated Iowa Reference Algorithm into 10 layers. Layer-specific mean intensities were determined and compared between the patient and control groups using multiple regression analysis while adjusting for age and optical intensity of the entire region ...

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    21. Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images with Serous Pigment Epithelial Detachments

      Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images with Serous Pigment Epithelial Detachments

      Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We pro-pose a method to automatically segment the retinal layers in 3 - D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset ...

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    22. An Automated Framework of Inner Segment/Outer Segment Defect Detection for Retinal SD-OCT Images

      An Automated Framework of Inner Segment/Outer  Segment Defect Detection for Retinal SD-OCT Images

      The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of 1mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was ...

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    23. Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

      Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

      Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details ...

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    24. Support vector machine based IS/OS disruption detection from SD-OCT images

      Support vector machine based IS/OS disruption detection from SD-OCT images

      In this paper, we sought to find a method to detect the Inner Segment /Outer Segment (IS/OS)disruption region automatically. A novel support vector machine (SVM) based method was proposed for IS/OS disruption detection. The method includes two parts: training and testing. During the training phase, 7 features from the region around the fovea are calculated. Support vector machine (SVM) is utilized as the classification method. In the testing phase, the training model derived is utilized to classify the disruption and non-disruption region of the IS/OS, and calculate the accuracy separately. The proposed method was tested on ...

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      Mentions: Haoyu Chen
    1-24 of 29 1 2 »
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