Despeckling Methods Research Articles

An iterative speckle filtering algorithm for ultrasound images based on bayesian nonlocal means filter model

Abstract In this paper, we study the problem of suppressing speckle noise in ultrasound images for better clinical diagnosis and subsequent image processes. In order to employ Bayesian nonlocal means filter (BNLMF) model in the circumstance of speckle noise to realize image restoration, we deduce the key probability density function with the help of speckle noise statistical characteristic and then present an iterative filtering algorithm. The first iteration with the noisy image itself being the input of the filtering model generates an initial estimator of the clean image which then further offers a better input of the filtering model. The constantly updated neighbor patches and probability density functions make the filtering result closer to the potential clean one. The healthy iteration process exports the favorable restored image surpassing the results obtained by some typical despeckling methods. Besides, benefit from the blockwise filtering style, pre-patch-selection operation and a small iteration number, the whole algorithm won't consume much time. Various experiments designed for processing the simulated noisy images and the real ultrasound images prove the superiority of the proposed method.

Strategies for reducing speckle noise in digital holography

Digital holography (DH) has emerged as one of the most effective coherent imaging technologies. The technological developments of digital sensors and optical elements have made DH the primary approach in several research fields, from quantitative phase imaging to optical metrology and 3D display technologies, to name a few. Like many other digital imaging techniques, DH must cope with the issue of speckle artifacts, due to the coherent nature of the required light sources. Despite the complexity of the recently proposed de-speckling methods, many have not yet attained the required level of effectiveness. That is, a universal denoising strategy for completely suppressing holographic noise has not yet been established. Thus the removal of speckle noise from holographic images represents a bottleneck for the entire optics and photonics scientific community. This review article provides a broad discussion about the noise issue in DH, with the aim of covering the best-performing noise reduction approaches that have been proposed so far. Quantitative comparisons among these approaches will be presented.

Distributed compressed sensing for despeckling of SAR images

Speckle noise is one of the critical disturbances that present in the radar imagery. This noise degrades the quality of synthetic aperture radar (SAR) images and needs to be reduced before using SAR images. This paper investigates a novel method for despeckling of SAR images in the distributed compressed sensing (DCS) framework. A linear matrix-based formulation is developed for the received SAR raw data and a compressive measurement and partitioning (CMP) scheme is proposed to collect and partition the SAR data into some data subsets. Then, a DCS-based despeckling method is proposed, in which, all data subsets are jointly considered in image formation and noise reduction. One of the important features of the proposed method is that the image formation and speckle reduction are done jointly. Finally, experimental results are provided to show the effectiveness of the proposed method in comparison to the previous ones.

Statistical modeling and Gaussianization procedure based de-speckling algorithm for retinal OCT images

This paper presents a de-noising method for speckle noise removal called de-speckling, retinal optical coherence tomography image by combining the features of wavelet transform, statistical modeling, Bayesian estimators and Gaussianization procedure. De-speckling methods based on statistical modeling of wavelet coefficients depend on the correct estimation of probability density function (PDF). The density estimation problem has been solved by using the Gaussianization procedure. The dynamic range capability of Gaussian PDF has been used for sharp estimation of density. A Cauchy PDF is used for modeling wavelet coefficients and finding the cumulative distribution function (CDF) of wavelet coefficients. This CDF is used as the input to the Gaussianization procedure, where it equalizes with the Gaussian CDF to estimate the density function of wavelet coefficients. Finally, the wavelet coefficients are recovered using Bayesian minimum mean square error estimator. Both visual and quantitative comparisons are performed for demonstrating the prominence of the proposed method. From simulation result it is seen that the proposed method outperforms the state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity, correlation coefficient (CoC) and edge preservation index (EPI). The proposed method has been achieved improvement of 5.13% in PSNR, 2.44% in SSIM, 2.11% in CoC and 3.45% in EPI over the well accepted existing method.

Speckle noise reduction for optical coherence tomography based on adaptive 2D dictionary

As a high-resolution biomedical imaging modality, optical coherence tomography (OCT) is widely used in medical sciences. However, OCT images often suffer from speckle noise, which can mask some important image information, and thus reduce the accuracy of clinical diagnosis. Taking full advantage of nonlocal self-similarity and adaptive 2D-dictionary-based sparse representation, in this work, a speckle noise reduction algorithm is proposed for despeckling OCT images. To reduce speckle noise while preserving local image features, similar nonlocal patches are first extracted from the noisy image and put into groups using a gamma- distribution-based block matching method. An adaptive 2D dictionary is then learned for each patch group. Unlike traditional vector-based sparse coding, we express each image patch by the linear combination of a few matrices. This image-to-matrix method can exploit the local correlation between pixels. Since each image patch might belong to several groups, the despeckled OCT image is finally obtained by aggregating all filtered image patches. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual inspection.

Feature-preserving ultrasound speckle reduction via L 0 minimization

Abstract Speckle reduction is a crucial prerequisite of many computer-aided ultrasound diagnosis and treatment systems. However, most existing speckle reduction filters tend to concentrate the blurring near the features and introduce the hole artifacts, making the subsequent processing procedures complicated. Optimization-based methods can globally distribute such blurring, leading to better feature preservation. Motivated by this, we propose a novel optimization framework based on L0 minimization for feature preserving ultrasound speckle reduction. We present an observation that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images (output) than in speckled images (input). Based on this observation, we propose an L0 minimization framework to remove speckle noise and simultaneously preserve features in the ultrasound images. It seeks for the L0 sparsity of the GAP values, and such sparsity is achieved by reducing small GAP values to zero in an iterative manner. Since features have larger GAP magnitudes than speckle noise, the proposed L0 minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of GAP values corresponding to prominent features are kept unchanged, leading to better preservation of those features. In addition, we propose an efficient and robust numerical scheme to transform the original intractable L0 minimization into several sub-optimizations, from which we can quickly find their closed-form solutions. Experiments on synthetic and clinical ultrasound images demonstrate that our approach outperforms other state-of-the-art despeckling methods in terms of noise removal and feature preservation.

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.

Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling

BackgroundUltrasound imaging is safer than other imaging modalities, because it is noninvasive and nonradiative. Speckle noise degrades the quality of ultrasound images and has negative effects on visual perception and diagnostic operations.MethodsIn this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed. A spatiogram similarity measurement is introduced for the similarity calculation between image patches. It is based on symmetric Kullback-Leibler (KL) divergence and signal-dependent speckle model for log-compressed ultrasound images. Each patch is regarded as a spatiogram, and the spatial distribution of each bin of the spatiogram is regarded as a weighted Gamma distribution. The similarity between the corresponding bins of the two spatiograms is computed by the symmetric KL divergence. The Split-Bregman fast algorithm is then used to solve the adapted NLTV object function. Kolmogorov-Smirnov (KS) test is performed on synthetic noisy images and real ultrasound images.ResultsWe validate our method on synthetic noisy images and clinical ultrasound images. Three measures are adopted for the quantitative evaluation of the despeckling performance: the signal-to-noise ratio (SNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). For synthetic noisy images, when the noise level increases, the proposed algorithm achieves slightly higher SNRS than that of the other two algorithms, and the SSIMS yielded by the proposed algorithm is obviously higher than that of the other two algorithms. For liver, IVUS and 3DUS images, the NIQE values are 8.25, 6.42 and 9.01, all of which are higher than that of the other two algorithms.ConclusionsThe results of the experiments over synthetic and real ultrasound images demonstrate that the proposed method outperforms current state-of-the-art despeckling methods with respect to speckle reduction and tissue texture preservation.

Feature-Based Nonlocal Polarimetric SAR Filtering

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV ) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

A Nonlinear Diffusion Equation-Based Model for Ultrasound Speckle Noise Removal

Ultrasound images are contaminated by speckle noise, which brings difficulties in further image analysis and clinical diagnosis. In this paper, we address this problem in the view of nonlinear diffusion equation theories. We develop a nonlinear diffusion equation-based model by taking into account not only the gradient information of the image, but also the information of the gray levels of the image. By utilizing the region indicator as the variable exponent, we can adaptively control the diffusion type which alternates between the Perona–Malik diffusion and the Charbonnier diffusion according to the image gray levels. Furthermore, we analyze the proposed model with respect to the theoretical and numerical properties. Experiments show that the proposed method achieves much better speckle suppression and edge preservation when compared with the traditional despeckling methods, especially in the low gray level and low-contrast regions.

The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method

ABSTRACTSpeckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy.

Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique

Abstract Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images.

Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts

Automated 3D breast ultrasound (ABUS) is a new popular modality as an adjunct to mammography for detecting cancers in women with dense breasts. In this paper, a multi-stage computer aided detection system is proposed to detect cancers in ABUS images. In the first step, an efficient despeckling method called OBNLM is applied on the images to reduce speckle noise. Afterwards, a new algorithm based on isocontours is applied to detect initial candidates as the boundary of masses is hypo echoic. To reduce false generated isocontours, features such as hypoechoicity, roundness, area and contour strength are used. Consequently, the resulted candidates are further processed by a cascade classifier whose base classifiers are Random Under-Sampling Boosting (RUSBoost) that are introduced to deal with imbalanced datasets. Each base classifier is trained on a group of features like Gabor, LBP, GLCM and other features. Performance of the proposed system was evaluated using 104 volumes from 74 patients, including 112 malignant lesions. According to Free Response Operating Characteristic (FROC) analysis, the proposed system achieved the region-based sensitivity and case-based sensitivity of 68% and 76% at one false positive per image.

Real-Time Nonlocal Means-Based Despeckling.

In this paper, we propose a multiscale nonlocal means-based despeckling method for medical ultrasound. The multiscale approach leads to large computational savings and improves despeckling results over single-scale iterative approaches. We present two variants of the method. The first, denoted multiscale nonlocal means (MNLM), yields uniform robust filtering of speckle both in structured and homogeneous regions. The second, denoted unnormalized MNLM (UMNLM), is more conservative in regions of structure assuring minimal disruption of salient image details. Due to the popularity of anisotropic diffusion-based methods in the despeckling literature, we review the connection between anisotropic diffusion and iterative variants of NLM. These iterative variants in turn relate to our multiscale variant. As part of our evaluation, we conduct a simulation study making use of ground truth phantoms generated from clinical B-mode ultrasound images. We evaluate our method against a set of popular methods from the despeckling literature on both fine and coarse speckle noise. In terms of computational efficiency, our method outperforms the other considered methods. Quantitatively on simulations and on a tissue-mimicking phantom, our method is found to be competitive with the state-of-the-art. On clinical B-mode images, our method is found to effectively smooth speckle while preserving low-contrast and highly localized salient image detail.

Comparative study of statistical modeling of wavelet coefficients for SAR image despeckling

The work is concentrated to get good despeckled performance under non-homomorphic framework by involving simple distributions to obtain closed form Maximum-a-Posteriori probability (MAP) solutions. To estimate noise-free wavelet coefficient, distributions with lesser number of parameters allied Laplacian/Gaussian probability density function (pdf) for signal reflectivity and Rayleigh/Gaussian pdf for noisy signal are employed. Thus, the four despeckling methods are formed, namely LRMAP, GRMAP, LGMAP and GGMAP to despeckle SAR images and the effectiveness of different distributions is studied. The despeckling method is made adaptive by estimating the local variance of high frequency image using wavelet sub-band coefficient statistics. Also, the parameter space used in the proposed methods does not involve initialization, iterative search and convergence problems. The performances of the proposed methods are evaluated in terms of Equivalent Number of Looks (ENL), Peak Signal to Noise Ratio (PSNR), Edge Preservation ([Formula: see text] and Mean Structural Similarity Index Measure (MSSIM). Experimental results show that LRMAP yields good results over all methods and GGMAP does not perform good for all images in terms of all quality metrics. Also, the proposed methods yield good quality metrics in less computing time as compared with the method available in the literature.

Fuzzy inference rule based image despeckling using adaptive maximum likelihood estimation

Despite multitude of research ponders on despeckling ultrasound images, contriving an efficient despeckling method still exist as an open challenge. The presence of speckle noise in ultrasound images complicates the accuracy of disease diagnosis. The classical Rayleigh Maximum Likelihood Estimation (RMLE) based despeckling filter was exclusively proposed to despeckle ultrasound images. It provides proper tradeoff between speckle suppression and edge preservation by discriminating the image region as edge and background correspondingly. Many despeckling filters utilize various statistical measures which remains uncertain in order to distinguish between image edge region and background region. This proposed filter harness the Fuzzy inference rules based image connectivity measure to avoid ambiguity in image region discrimination and adapts an appropriate tuning parameter to modify classical RMLE despeckling. The proposed method involves three steps in which the first step uses Fuzzy inference rules to categorize the type of image area as edge and background. Second step involves recursive optimal selection of appropriate filter tuning parameter by using adaptive technique. Third step involves estimation of noise-free pixels by using RMLE formulation. Quantitative evaluation was made by considering various performance metrics to compare the proposed filter and existing filters. The obtained result demonstrates that the proposed filter predominantly preserves the edges structures and clinical features.

A SAR Image Despeckling Method Based on Two-Dimensional S Transform Shrinkage

Speckle is a granular disturbance that affects synthetic aperture radar (SAR) images. Over the last three decades, many methods have been proposed for speckle reduction, where a tradeoff between despeckling and detail preservation is required. As an attempt to balance the performance on both sides, in this paper, we propose a 2-D S transform shrinkage algorithm using adaptive soft threshold for SAR image despeckling. It follows the idea of the wavelet shrinkage algorithm, but extends its major steps to take into account the peculiarities of S transform, i.e., adding adaptivity in the estimation of speckle standard deviation and threshold function, in an optimized computation procedure. Homogeneous and heterogeneous SAR images are used for quantitative evaluations, and both vintage and prevailing algorithms are used for comparison, which demonstrates the validity of the proposed method. Additionally, some instructive pieces of advice are given on the selection of suitable parameters of the proposed method under different circumstances.

Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography

Purpose: This paper presents an evolutionary segmentation approach for breast lesions on ultrasound (BUS) based on the pulse-coupled neural network (PCNN) model and an adaptive differential evolution algorithm (JADE). Methods: First, a despeckle method based on anisotropic diffusion filtering is applied. Thereafter, to attenuate distant pixels that do not belong to the tumor, the filtered image is multiplied by a constraint Gaussian function. The resultant preprocessed image is segmented by a population of PCNNs whose parameters are evolved iteratively by JADE algorithm. Three population sizes are tested with 10, 20, and 50 individuals. The average radial derivative (ARD) function is used as a fitness function to be maximized. The experiments are performed on a BUS dataset with 100 ultrasonographs, which were delineated manually by a senior radiologist for defining ground-truth lesion outlines. The Matthews correlation coefficient (MCC) measures the accuracy of the proposed method, which evaluates the agreement between both the ground-truth and the computerized segmentations. Additionally, from segmented lesions, five morphological features are extracted and the local Fisher discriminant analysis (LFDA) classifier is trained. The 0.632+ bootstrap method is used for evaluating the classification performance in terms of the area under ROC curve (AUC). Results: The experimental results point out that segmentation accuracy is unaffected by the population size for attaining similar segmentation solutions, where the median MCC value is about 0.85. Moreover, the classification performance increased from manual delineation to PCNN segmentation (from AUC=0.907±0.032 to AUC=0.920±0.022). Conclusion: The evolutionary segmentation approach is capable of accurately segmenting and classifying BUS images with respect to ground-truth data. This behavior is because the JADE algorithm self-adapts the search procedure for finding adequate PCNN parameters that maximizes the ARD function.

Quantum‐inspired hybrid medical ultrasound images despeckling method

A novel medical ultrasound despeckling method combining quantum-inspired bilateral filtering and wavelet thresholding is proposed. An adaptive bilateral filter under the framework of quantum signal processing is exploited in this method and used as a pre-processing step. Then wavelet thresholding, on the assumption that the signal and noise coefficients in the wavelet domain are subject to generalised Laplace distribution and Gaussian distribution, respectively, is applied to the pre-filtered image. Experiments show that the proposed method can obtain a better speckle noise reduction performance while retaining high-contrast features such as edges.

Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images

Speckle noise is an undesirable part of the ultrasound imaging process, since it can degrade the quality of ultrasound images and restrict the development of automatic diagnostic techniques for ultrasound images. Aiming at the problem of speckle noise, an improved de-speckling method for medical ultrasound images is proposed, which is based on the wavelet transformation and fast bilateral filter. According to the statistical properties of medical ultrasound image in the wavelet domain, an improved wavelet threshold function based on the universal wavelet threshold function is considered. The wavelet coefficients of noise-free signal and speckle noise are modeled as generalized Laplace distribution and Gaussian distribution, respectively. The Bayesian maximum a posteriori estimation is applied to obtain a new wavelet shrinkage algorithm. High-pass component speckle noise in the wavelet domain of ultrasound images is suppressed by the new shrinkage algorithm. Additionally, the coefficients of the low frequency signal in the wavelet domain are filtered by the fast bilateral filter, since the low-pass component of ultrasound images also contains some speckle noise. Compared with other de-speckling methods, experiments show that the proposed method has improved de-speckling performance for medical ultrasound images. It not only has better reduction performance than other methods but also can preserve image details such as the edge of lesions.