Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images
TL;DR: A fast and accurate solution to speckle reduction targeted specifically at optical coherence tomography images, designed using a novel potential function based on the gradient of the local variance of intensity that results in removal of Speckle without destroying the edges of the desired object.
Abstract: We propose a fast and accurate solution to speckle reduction targeted specifically at optical coherence tomography images. The proposed speckle removing filter is designed using a novel potential function based on the gradient of the local variance of intensity. After filtering, the spatially neighboring pixels with close values of intensities converge to uniform gray values, while the edges remain intact. This filtering process results in removal of speckle without destroying the edges of the desired object. The proposed filter also prevents the generation of any false edges. Detailed experimental analysis shows at least 1-dB improvement in the peak signal-to-noise ratio for spectral domain optical coherence tomography images. The method also shows superior edge preservation, contrast, and speed compared to the state of the art in speckle removing filters.
Citations
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TL;DR: A new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images, which allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs.
20 citations
Cites methods from "Speckle Removal Using Diffusion Pot..."
...We can transform an image content into another domain such as filtering response domain [5] or multi-resolution domain [6,7], where image statistics can be modeled more efficiently....
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TL;DR: Qualitative and quantitative studies confirm that the seemingly subtle variation in model assumptions can have remarkable impact on despeckling.
Abstract: Speckle noise is a principal and unavoidable source of visual degradation in several real world images obtained from coherent imaging systems such as ultrasound, SAR, and laser. Over the la...
17 citations
Cites background or methods from "Speckle Removal Using Diffusion Pot..."
...Sub-sequentially, to improve the contrast and quality of edges in the despeckled image, Paul and Mukherjee [57] propose a novel potential function-based diffusion coefficient as Dt(x, y) = exp (−κt|ψ t(x, y)− ξ t(x, y)...
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...Apart form ENL, we also computed root-meansquared contrast (RMSC) to further look into contrasts of the despeckled images [57]....
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...Sub-sequentially, to improve the contrast and quality of edges in the despeckled image, Paul and Mukherjee [57] propose a novel potential function-based diffusion coefficient as Dt(x, y) = exp (−κt|ψ t(x, y)− ξ t(x, y)|) (9)...
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TL;DR: The experimental results prove that the proposed OCT super-resolution method based on the Laplacian+GEV model outperforms other competing methods in terms of both subjective and objective visual qualities.
Abstract: In this paper, a new statistical model is proposed for the single image super-resolution of retinal Optical Coherence Tomography (OCT) images. OCT imaging relies on interfero-metry, which explains why OCT images suffer from a high level of noise. Moreover, data subsampling is carried out during the acquisition of OCT A-scans and B-scans. So, it is necessary to utilize effective super-resolution algorithms to reconstruct high-resolution clean OCT images. In this paper, a nonlocal sparse model-based Bayesian framework is proposed for OCT restoration. For this reason, by characterizing nonlocal patches with similar structures, known as a group, the sparse coefficients of each group of OCT images are modeled by the scale mixture models. In this base, the coefficient vector is decomposed into the point-wise product of a random vector and a positive scaling variable. Estimation of the sparse coefficients depends on the proposed distribution for the random vector and scaling variable where the Laplacian random vector and Generalized Extreme-Value (GEV) scale parameter (Laplacian+GEV model) show the best goodness of fit for each group of OCT images. Finally, a new OCT super-resolution method based on this new scale mixture model is introduced, where the maximum a posterior estimation of both sparse coefficients and scaling variables are calculated efficiently by applying an alternating minimization method. Our experimental results prove that the proposed OCT super-resolution method based on the Laplacian+GEV model outperforms other competing methods in terms of both subjective and objective visual qualities.
11 citations
Cites methods from "Speckle Removal Using Diffusion Pot..."
...Numerous methods have been developed based on various image models for the superresolution of OCT images in the transform and spatial domains [8], [10], [12]–[21]....
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TL;DR: In this article, a patch-wise training methodology was proposed to denoise OCT images from different OCT machines using the publicly available DUKE and OPTIMA datasets to build a more efficient model for noise reduction.
9 citations
18 Dec 2018
TL;DR: A new method for denoising OCT images based on Convolutional Neural Network by learning common features from unpaired noisy and clean OCT images in an unsupervised, end-to-end manner is proposed.
Abstract: Optical coherence tomography (OCT) images are corrupted by speckle noise due to underlying coherence-based strategy. Speckle suppression/removal in OCT images plays a significant role in both manual and automatic detection of diseases, especially in early clinical diagnosis. In this paper, we propose a new method for denoising OCT images based on Convolutional Neural Network by learning common features from unpaired noisy and clean OCT images in an unsupervised, end-to-end manner. The proposed method consists of a combination of two autoencoders with shared encoder layers, which we call as Shared Encoder (SE) architecture. The SE is trained to reconstruct noisy and clean OCT images with respective autoencoders. The denoised OCT image is obtained using a cross-model prediction. The proposed method can be used for denoising OCT images with or without pathology from any scanner. The SE architecture was assessed using public datasets and found to perform better than baseline methods exhibiting a good balance of retaining anatomical integrity and speckle reduction.
8 citations
References
More filters
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
"Speckle Removal Using Diffusion Pot..." refers methods in this paper
...The edges in the reference images and the corresponding denoised images (by various methods) are found using Canny edge detection technique [26]....
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TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
12,560 citations
"Speckle Removal Using Diffusion Pot..." refers background in this paper
...Standard anisotropic diffusion [14] encourages the production of piecewise constant homogeneous regions....
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...Inspection of (7) reveals the advantage of the proposed method compared to the conventional anisotropic diffusion [14], which suffers from generation of false edges in nearhomogeneous regions with ripple (i....
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...Some of the more recent techniques tackle the problem of speckle reduction by updating partial differential equations in the form of anisotropic diffusion [14]....
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TL;DR: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications, and validates the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery.
Abstract: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.
1,816 citations
"Speckle Removal Using Diffusion Pot..." refers background or methods in this paper
...According to [15], q0(t) ≈ q0 exp [−t], where q0 is the speckle coefficient of variation....
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...reducing anisotropic diffusion (SRAD) [15]....
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...In particular, we compare our method with SRAD [15], detail preserving anisotropic diffusion (DPAD) [16], OSRAD [17], optimized Bayesian nonlocal means (OBNLM) [19], the method proposed in [21], ADMSS [22] and sparsity based simultaneous denoising and interpolation (SBSDI) method [20]....
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...Then using [15] we find that in our method: V (L(x, y)) ≈ V0(x, y) exp [−ρt] , (12)...
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...Yu and Acton [15] proposed an anisotropic diffusion using the coefficient of variation for speckle reduction....
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Book•
05 Dec 2013
TL;DR: Diagnostic Ultrasound Imaging provides a unified description of the physical principles of ultrasound imaging, signal processing, systems and measurements that enable practicing engineers, students and clinical professionals to understand the essential physics and signal processing techniques behind modern imaging systems.
Abstract: Diagnostic Ultrasound Imaging provides a unified description of the physical principles of ultrasound imaging, signal processing, systems and measurements. This comprehensive reference is a core resource for both graduate students and engineers in medical ultrasound research and design. With continuing rapid technological development of ultrasound in medical diagnosis, it is a critical subject for biomedical engineers, clinical and healthcare engineers and practitioners, medical physicists, and related professionals in the fields of signal and image processing. The book contains 17 new and updated chapters covering the fundamentals and latest advances in the area, and includes four appendices, 450 figures (60 available in color on the companion website), and almost 1,500 references. In addition to the continual influx of readers entering the field of ultrasound worldwide who need the broad grounding in the core technologies of ultrasound, this book provides those already working in these areas with clear and comprehensive expositions of these key new topics as well as introductions to state-of-the-art innovations in this field. * Enables practicing engineers, students and clinical professionals to understand the essential physics and signal processing techniques behind modern imaging systems as well as introducing the latest developments that will shape medical ultrasound in the future* Suitable for both newcomers and experienced readers, the practical, progressively organized applied approach is supported by hands-on MATLAB code and worked examples that enable readers to understand the principles underlying diagnostic and therapeutic ultrasound* Covers the new important developments in the use of medical ultrasound: elastography and high-intensity therapeutic ultrasound. Many new developments are comprehensively reviewed and explained, including aberration correction, acoustic measurements, acoustic radiation force imaging, alternate imaging architectures, bioeffects: diagnostic to therapeutic, Fourier transform imaging, multimode imaging, plane wave compounding, research platforms, synthetic aperture, vector Doppler, transient shear wave elastography, ultrafast imaging and Doppler, functional ultrasound and viscoelastic models
1,170 citations
"Speckle Removal Using Diffusion Pot..." refers background in this paper
...Speckle occurs due to multiple scattering and phase aberrations of the propagating beam used for imaging [1], [5], [6]....
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TL;DR: Four speckle-reduction methods-polarization diversity, spatialcompounding, frequency compounding, and digital signal processing-are discussed and the potential effectiveness of each method is analyzed briefly with the aid of examples.
Abstract: Speckle arises as a natural consequence of the limited spatial-frequency bandwidth of the interference signals measured in optical coherence tomography (OCT). In images of highly scattering biological tissues, speckle has a dual role as a source of noise and as a carrier of information about tissue microstructure. The first half of this paper provides an overview of the origin, statistical properties, and classification of speckle in OCT. The concepts of signal-carrying and signal-degrading speckle are defined in terms of the phase and amplitude disturbances of the sample beam. In the remaining half of the paper, four speckle-reduction methods-polarization diversity, spatial compounding, frequency compounding, and digital signal processing-are discussed and the potential effectiveness of each method is analyzed briefly with the aid of examples. Finally, remaining problems that merit further research are suggested. © 1999 Society of Photo-Optical Instrumentation Engineers.
886 citations
"Speckle Removal Using Diffusion Pot..." refers background in this paper
...Speckle occurs due to multiple scattering and phase aberrations of the propagating beam used for imaging [1], [5], [6]....
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