Noise In Optical Coherence Tomography Images Research Articles

Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising

Optical coherence tomography (OCT) has been extensively utilized in the field of biomedical imaging due to its non-invasive nature and its ability to provide high-resolution, in-depth imaging of biological tissues. However, the use of low-coherence light can lead to unintended interference phenomena within the sample, which inevitably introduces speckle noise into the imaging results. This type of noise often obscures key features in the image, thereby reducing the accuracy of medical diagnoses. Existing denoising algorithms, while removing noise, tend to also damage the structural details of the image, affecting the quality of diagnosis. To overcome this challenge, we have proposed a speckle noise (PSN) framework. The core of this framework is an innovative dual-module noise generator that can decompose the noise in OCT images into speckle noise and equipment noise, addressing each type independently. By integrating the physical properties of noise into the design of the noise generator and training it with unpaired data, we are able to synthesize realistic noise images that match clear images. These synthesized paired images are then used to train a denoiser to effectively denoise real OCT images. Our method has demonstrated its superiority in both private and public datasets, particularly in maintaining the integrity of the image structure. This study emphasizes the importance of considering the physical information of noise in denoising tasks, providing a new perspective and solution for enhancing OCT image denoising technology.

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9dB, CNR to 6.3dB, SSIM to 0.9, and equivalent number of looks to 120.8dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7dB, respectively. The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.

Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe

Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT.

Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices.

A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.

Multi-task generative adversarial network for retinal optical coherence tomography image denoising

Objective. Optical coherence tomography (OCT) has become an essential imaging modality for the assessment of ophthalmic diseases. However, speckle noise in OCT images obscures subtle but important morphological details and hampers its clinical applications. In this work, a novel multi-task generative adversarial network (MGAN) is proposed for retinal OCT image denoising. Approach. To strengthen the preservation of retinal structural information in the OCT denoising procedure, the proposed MGAN integrates adversarial learning and multi-task learning. Specifically, the generator of MGAN simultaneously undertakes two tasks, including the denoising task and the segmentation task. The segmentation task aims at the generation of the retinal segmentation map, which can guide the denoising task to focus on the retina-related region based on the retina-attention module. In doing so, the denoising task can enhance the attention to the retinal region and subsequently protect the structural detail based on the supervision of the structural similarity index measure loss. Main results. The proposed MGAN was evaluated and analyzed on three public OCT datasets. The qualitative and quantitative comparisons show that the MGAN method can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous denoising methods. Significance. We have presented a MGAN for retinal OCT image denoising. The proposed method provides an effective way to strengthen the preservation of structural information while suppressing speckle noise, and can promote the OCT applications in the clinical observation and diagnosis of retinopathy.

Non-Local Mean Denoising Algorithm Based on Fractional Compact Finite Difference Scheme Effectively Reduces Speckle Noise in Optical Coherence Tomography Images.

Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address this problem, a non-local means (NLM) algorithm based on the fractional compact finite difference scheme (FCFDS) is proposed to remove the speckle noise in OCT images. FCFDS uses more local pixel information when compared to integer-order difference operators. The FCFDS operator is introduced into the NLM algorithm to construct a high-precision weight calculation so that the proposed algorithm can effectively reduce the speckle noise in the OCT images. Experiments on simulations and real OCT images show that the proposed method is comparable to other state-of-the-art despeckling methods and can substantially reduce noise and preserve image details such as edges and structures. Speckle noise removal can further promote the application of the proposed algorithm in medical diagnosis and industrial detection, as it has key research value.

Optical coherence tomography image despeckling based on edge feature-guided higher-order singular value decomposition

As an emerging tomographic technique, optical coherence tomography (OCT) has been widely used in clinical practice in a short period of time. Unfortunately, the quality of OCT images is inevitably deteriorated by the coherent noise known as speckle noise. To effectively reduce the speckle noise in OCT images while preserving local structures, we present a speckle noise reduction method based on feature-guided higher-order singular value decomposition (HOSVD). Specifically, the grouped 3D tensors are first decomposed by HOSVD. And then, the corresponding core tensors are thresholded according to the Eckart-Young-Mirsky theorem and are shrunk by the dectected edge features, which are defined as the local mean absolute deviation (LMAD). Finally, the filtered OCT images can be obtained by the inverse transform and an aggregation strategy. Compared with several state-of-the-art methods, the proposed method has better performance on despeckling OCT images in terms of both speckle noise reduction and edge preservation.

DHNet: High-resolution and hierarchical network for cross-domain OCT speckle noise reduction.

Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high-resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end-to-end denoising framework for OCT images collected by differentscanners. The proposed model utilizes the high-resolution network (HRNet) as backbone for image restoration, which reconstructs high-fidelity images by maintaining high-resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV-1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double-H-Net, High-resolution and Hierarchical Network). We compare DHNet with state-of-the-art methods and experiment results show that DHNet improves signal-to-noise ratio by a large margin of 18.137 dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25 ms, which satisfies the real-time processing requirement for the BV-1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improvesegmentation. The proposed DHNet can compensate domain shifts between different data sets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low- and high-resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25 ms, which satisfied the real-time processingrequirement.

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network.

Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT images, a denoising algorithm based on a convolutional neural network is proposed in this paper. Unlike traditional algorithms that directly obtain denoised images, the algorithm model proposed in this paper learns the speckle noise distribution through the constructed trainable OCT dataset and indirectly obtains the denoised result image. In order to verify the performance of the model, we compare the denoising results of the algorithm proposed in this paper with several state-of-the-art algorithms from three perspectives: qualitative evaluation from the subjective visual perspective, quantitative evaluation from objective parameter indicators, and running time. The experimental results show that the proposed algorithm has a good denoising effect on different OCT images of the anterior segment and has good generalization ability. Besides, it retains the relevant details and texture information in the image, and it has strong edge preserving ability. The image of OCT speckle removal can be obtained within 0.4 s, which meets the time limit requirement of clinical application.

Noise reduction by adaptive-SIN filtering for retinal OCT images

Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose an adaptive denoising algorithm for OCT images. The OCT noise is closer to the Poisson distribution than the Gaussian distribution, and shearlet transform assumes additive white Gaussian noise. We hence propose a square-root transform to redistribute the OCT noise. Different manufacturers and differences between imaging objects may influence the observed noise characteristics, which make predefined thresholding scheme ineffective. We propose an adaptive 3D shearlet image filter with noise-redistribution (adaptive-SIN) scheme for OCT images. The proposed adaptive-SIN is evaluated on three benchmark datasets using quantitative evaluation metrics and subjective visual inspection. Compared with other algorithms, the proposed algorithm better removes noise in OCT images and better preserves image details, significantly outperforming in terms of both quantitative evaluation and visual inspection. The proposed algorithm effectively transforms the Poisson noise to Gaussian noise so that the subsequent shearlet transform could optimally remove the noise. The proposed adaptive thresholding scheme optimally adapts to various noise conditions and hence better remove the noise. The comparison experimental results on three benchmark datasets against 8 compared algorithms demonstrate the effectiveness of the proposed approach in removing OCT noise.

Deep feature loss to denoise OCT images using deep neural networks.

.Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images.Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN).Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of and . The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses.Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses ( and ) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, , and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively.Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.

Speckle Noise Reduction for OCT Images Based on Image Style Transfer and Conditional GAN.

Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In our previous study(Ma et al., 2018), we have developed a Conditional Generative Adversarial Network (cGAN) for speckle noise removal in OCT images collected by several commercial OCT scanners, which we collectively refer to as scanner T. In this paper, we improve the cGAN model and apply it to our in-house OCT scanner (scanner B) for speckle noise suppression. The proposed model consists of two steps: 1) We train a Cycle-Consistent GAN (CycleGAN) to learn style transfer between two OCT image datasets collected by different scanners. The purpose of the CycleGAN is to leverage the ground truth dataset created in our previous study. 2) We train a mini-cGAN model based on the PatchGAN mechanism with the ground truth dataset to suppress speckle noise in OCT images. After training, we first apply the CycleGAN model to convert raw images collected by scanner B to match the style of the images from scanner T, and subsequently use the mini-cGAN model to suppress speckle noise in the style transferred images. We evaluate the proposed method on a dataset collected by scanner B. Experimental results show that the improved model outperforms our previous method and other state-of-the-art models in speckle noise removal, retinal structure preservation and contrast enhancement.

Diagnosis of Age Related Macular Degeneration by Curve Fitting RPE Layer

Optical Coherence Tomography (OCT) of the retina allow high resolution and non-invasive imaging for diagnosis of macular diseases such as Age Related Macular Degeneration (ARMD). The aim of this work is to suppress the speckle noise, segment the retinal pigment epithelium layer and identify the possibilities for diagnosis of ARMD by curve fitting method. Speckle pattern, which is multiplicative in nature, degrades the quality of OCT images. Bilateral and homomorphic-wavelet filters are adopted to minimize the speckle noise in OCT images. The performance of these filters is tested on a set of OCT images collected from an open source database. The experimental results prove that homomorphic-wavelet filter is better in reducing the speckle noise. Structural changes of retinal pigment epithelium layer, a key factor of ARMD, is identified by Graph-based method, which is utilized further in diagnosis of ARMD by curve fitting method. The sum of squares due to error (SSE) values of ARMD images are very higher compared to normal images, which can be used as a feature for diagnosis.

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT denoising methods and other state-of-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

Speckle denoising in optical coherence tomography images using residual deep convolutional neural network

Optical Coherence Tomography (OCT) is an emerging imaging modality used for diagnosis of ocular diseases like age-related macular degeneration (AMD) and macular edema. OCT imaging is a non-invasive technique to capture cross-sectional volumes of the retinal areas of human eye. Due to coherent nature of image acquisition process, OCT images suffer from granular multiplicative speckle noise. Presence of speckle noise in OCT images makes its clinical analysis difficult for the experts. The same is the problem with the development of computer aided diagnosis (CAD) systems for detection of ocular diseases. Speckle noise is granular in nature and interferes with the diagnostic observations made using OCT images and the segmentation of different OCT layers. This work presents an efficient OCT denoising technique using residual convolutional neural network. The proposed technique will not only help experts in analysis of OCT images, but can also act as first step to construct CAD systems for ocular diseases. The performance of the proposed approach is evaluated on Duke (SD-OCT) and Topcon (3D-OCT) image databases based on visual and parametric observations. The performance of the proposed method on parameters like PSNR, SSIM, MSR, CNR, and ENL is compared with the state-of-the-art speckle denoising methods. It is observed that the proposed approach performs better as compared to the methods referred from literature on both visual and parametric evaluations.

BM3D-based total variation algorithm for speckle removal with structure-preserving in OCT images.

In this paper, we propose a total variation based on block matching 3D (BM3D-TV method), which includes the total variation regular term, the data fidelity term, and the block matching term. In addition, we also propose a fast numerical algorithm based on the split Bregman iteration for the proposed method. By assigning suitable weights to the data fidelity term and block matching term, the image noise reduction and the image structural characteristics can be matched optimally. We test the proposed method on six human retinal and one mouse skin optical coherence tomography (OCT) images respectively, and also compare it with total variation (TV) and BM3D, which were proved to be effective in denoising. The performances of these methods are quantitatively evaluated in terms of the signal-to-noise ratio, the contrast-to-noise ratio, and the averaged equivalent number of homogeneous areas at the aspects of speckle reduction and structure protection. Vast experiments show that the BM3D-TV method can effectively reduce speckle noise in OCT images, protect important structural information and improve image quality, compared with the BM3D and TV methods.

OCT image speckle sparse noise reduction based on dictionary algorithm

As a new non-invasive and high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.

An Experimentally Trained Noise Filtration Method of Optical Coherence Tomography Signals

A method for wavelet filtration procedure training for optical coherence tomography (OCT) images using the experimental measurements of test objects that were constructed by means of water solutions of monodisperse nanoparticles and several microscopic inclusions has been described in the present paper. The choice of test-object parameters (concentration of water solution, size of nanoparticles, and shape, dimensions, and mutual position of inclusions) has allowed the modeling of various working conditions of OCT and setting different criteria for estimation of filtration efficiency. In the present work, the optimal filter for the considered example of a test object has been selected among the combinations of various basic functions of five wavelet families, soft and hard threshold filtering methods, four decomposition levels, and threshold values in a range of 0.05–3.05. The mutual position of the micro-inclusions has been used as a criterion for evaluating the filtration efficiency. As a result, it has been shown that the determined wavelet filter leads to effective suppression of the scattering noise in OCT images and preserve information about the structure of the object under study.

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AbstractA method for wavelet filtration procedure training for optical coherence tomography (OCT) images using the experimental measurements of test objects that were constructed by means of water solutions of monodisperse nanoparticles and several microscopic inclusions has been described in the present paper. The choice of test-object parameters (concentration of water solution, size of nanoparticles, and shape, dimensions, and mutual position of inclusions) has allowed the modeling of various working conditions of OCT and setting different criteria for estimation of filtration efficiency. In the present work, the optimal filter for the considered example of a test object has been selected among the combinations of various basic functions of five wavelet families, soft and hard threshold filtering methods, four decomposition levels, and threshold values in a range of 0.05–3.05. The mutual position of the micro-inclusions has been used as a criterion for evaluating the filtration efficiency. As a result, it has been shown that the determined wavelet filter leads to effective suppression of the scattering noise in OCT images and preserve information about the structure of the object under study.

Vascular Deformation Analysis Based on in Vivo Intravascular Optical Coherence Tomography Imaging

Intravascular optical coherence tomography (OCT) has the characteristics of high resolution and fast imaging speed. Continuous images of the same section of the same vessel can reflect the deformation characteristics of the vessel wall under different blood pressure. Digital image processing may be used to segment various structures on the vascular wall and extract the deformation incorporating with biomechanical analysis. Image filtering plays a very important role in image processing. Median filter was used to filter salt and pepper noise in OCT images. Fuzzy function gray processing method was used to suppress irrelevant information and improve image clarity. Dividing point and line method was proposed to calculate the deformation and strain field.