Real Ultrasound Images Research Articles

Multi-ray medical ultrasound simulation without explicit speckle modelling.

To develop a medical ultrasound (US) simulation method using T1-weighted magnetic resonance images (MRI) as the input that offers a compromise between low-cost ray-based and high-cost realistic wave-based simulations. The proposed method uses a novel multi-ray image formation approach with a virtual phased array transducer probe. A domain model is built from input MR images. Multiple virtual acoustic rays are emerged from each element of the linear transducer array. Reflected and transmitted acoustic energy at discrete points along each ray is computed independently. Simulated US images are computed by fusion of the reflected energy along multiple rays from multiple transducers, while phase delays due to differences in distances to transducers are taken into account. A preliminary implementation using GPUs is presented. Preliminary results show that the multi-ray approach is capable of generating view point-dependent realistic US images with an inherent Rician distributed speckle pattern automatically. The proposed simulator can reproduce the shadowing artefacts and demonstrates frequency dependence apt for practical training purposes. We also have presented preliminary results towards the utilization of the method for real-time simulations. The proposed method offers a low-cost near-real-time wave-like simulation of realistic US images from input MR data. It can further be improved to cover the pathological findings using an improved domain model, without any algorithmic updates. Such a domain model would require lesion segmentation or manual embedding of virtual pathologies for training purposes.

Speckle suppression in medical ultrasound images through Schur decomposition

A technique based on Schur decomposition to supress the multiplicative (speckle) noise from medical ultrasound images is presented in this study. An image which carries the speckle noise is divided into small overlapping segments, size of these segments depends on the nature of speckle carried by the image and a global covariance matrix is calculated for the whole image by averaging the covariances of all segments. The global covariance matrix is decomposed through Schur decomposition to obtain the orthogonal vectors. A subset of these orthogonal vectors that correspond to largest magnitudes of eigenvalues are selected to filter out the speckle noise from the image. The proposed approach is compared with four benchmark filtering techniques, homomorphic wavelet despeckling, Wiener, Frost and Gamma. Two types of simulated ultrasound images and five types of real ultrasound images of foetal neck, left kidney, right kidney, musculo skeletal nerve and lymph node are tested. The proposed approach performed maximum suppression of speckle noise in all types of the images with optimal resolution and edge detection. The despeckling performance of the proposed approach is even better compared with the benchmark schemes once the speckle noise is rough, which is usually the case for soft tissue.

Combination of Spatial Domain Filters for Speckle Noise Reduction in Ultrasound Medical Images

The occurrence of speckle noise in medical Ultrasound (US) images poses a big challenge to medical practitioners over last several years. Speckle noise reduces the fine details present in the images and hence make it more difficult to diagnose. In this paper, a~novel method based on the combination of three spatial domain filters is presented. The output of these filters is combined on the basis of an Intensity Classifier Map (ICF) formed using Coefficient of Dispersion (CoD) parameter. Experiments were conducted on synthetic and real US images. Quantitative and qualitative analysis of the proposed method is carried out in comparison to other six existing methods. It has been found from the obtained results that proposed method delivers observable improvement in all quantitative parameters undertaken for synthetic US image and in MVR value for real US images. Also, the effectiveness of the proposed method is found to be consistent with qualitative assessment of the denoised images.

Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modeling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

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.

An Approach for Despeckling a Sequence of Ultrasound Images Based on Statistical Analysis

In this paper, a denoising approach taking into account the statistical properties of the ultrasound image is proposed. In the proposed method, the time correlation between the different frames of a video sequence are taken into consideration to find the maximum a posteriori probability estimate of a noisy pixel. The proposed algorithm is tested on both synthetic and real ultrasound images to verify the effectiveness of the system. The paper provides a model for time correlation of the noise between different frames of the ultrasound video data. To simulate the speckle noise in a natural image, an attempt is made to study the probability density function of speckle noise in an ultrasound video sequence. From our analysis, it is concluded that the speckle noise is multiplicative Gaussian in nature. Certain statistical tests are also conducted to verify the effectiveness of our proposed method. Experimental results show that the proposed despeckling method shows superior performance than its counterparts.

Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion.

Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.

Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

As a particular case of the finite mixture model, Rayleigh mixture model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. The authors proposed an improved RMM with neighbour (RMMN) information to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. They evaluated their model on experiments on synthetic data and real US images used by high-intensity focused ultrasound therapy. On this data, they demonstrated that the proposed model outperforms several state-of-the-art methods in terms of both segmentation accuracy and computation time.

Realistic Ultrasound Simulation of Complex Surface Models Using Interactive Monte‐Carlo Path Tracing

AbstractRay‐based simulations have been shown to generate impressively realistic ultrasound images in interactive frame rates. Recent efforts used GPU‐based surface raytracing to simulate complex ultrasound interactions such as multiple reflections and refractions. These methods are restricted to perfectly specular reflections (i.e. following only a single reflective/refractive ray), whereas real tissue exhibits roughness of varying degree at tissue interfaces, causing partly diffuse reflections and refractions. Such surface interactions are significantly more complex and can in general not be handled by conventional deterministic raytracing approaches. However, these can be efficiently computed by Monte‐Carlo sampling techniques, where many ray paths are generated with respect to a probability distribution. In this paper, we introduce Monte‐Carlo raytracing for ultrasound simulation. This enables the realistic simulation of ultrasound‐tissue interactions such as soft shadows and fuzzy reflections. We discuss how to properly weight the contribution of each ray path in order to simulate the behaviour of a beamformed ultrasound signal. Tracing many individual rays per transducer element is easily parallelizable on modern GPUs, as opposed to previous approaches based on recursive binary raytracing. We further propose a significant performance optimization based on adaptive sampling.

A hybrid algorithm for speckle noise reduction of ultrasound images

Background and objectiveMedical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. MethodsThe performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. ResultsThe results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. ConclusionsThe denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images.

Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D

Ultrasound image despeckling is an important research field, since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this paper, we derive new stochastic distances for the Fisher-Tippett distribution, based on well-known statistical divergences, and use them as patch distance measures in a modified version of the BM3D algorithm for despeckling log-compressed ultrasound images. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach.

A Density Current Modeled Adaptive Weighted Average Despeckling Filter for Ultrasound Thyroid Images

Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, an adaptive weighted average despeckling algorithm is proposed based on density currents equation. An analogy between the gravity current density model for sea breeze and speckle noise affected image is used and a denoising filter is designed. Suitable threshold values to distinguish between different regions in the image are selected for the denoising filter by considering the structural similarity and mean square error parameters of the particular image. Hence the proposed denoising filter adaptively changes the threshold values depending on nature of image and magnitude of noise in the image. For each pixel, weights have been changed adaptively by considering local neighbours in different directions. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method outperforms other existing methods in terms of signal to noise ratio, Structural similarity index and Ultrasound despeckling assessment index. It is also shown that proposed filter is able to improve the visual quality of Ultrasound images and better preserves image structural details compared to existing methods.

Fast feature-preserving speckle reduction for ultrasound images via phase congruency

Ultrasonography is widely used in clinical diagnosis and therapeutic procedures, but speckle noise often obscures important features and complicates interpretation and analysis of ultrasound images. In this regard, speckle reduction is a crucial prerequisite of many computer aided ultrasound diagnosis and treatment systems However, removing speckle noise while simultaneously preserving features in ultrasound images is a challenging task. We propose a novel optimization framework for speckle reduction by leveraging the concept of phase congruency and incorporating a feature asymmetry metric into the regularization term of the objective function to effectively distinguish the features and speckle noise. The feature asymmetry metric can productively separate features from speckle noise by analyzing the local frequency information. Compared with traditional methods employing intensity gradients as regularization terms, our framework is invariant to the intensity amplitude of features so that low contrast features are almost equally protected as high contrast features. In addition, rather than adopting the gradient descent, we propose a novel solver by decomposing the original non-convex optimization into solving several linear systems, leading to an efficient solution of the optimization. Owing to different penalties on speckle noise and features, our method can efficiently remove speckle noise and preserve features at the same time. Experiments on simulated and real ultrasound images demonstrate our method can better maintain features with speckle removal than state-of-the-art methods, especially for the low contrast features.

Non-Gaussian amplitude PDF modeling of ultrasound images based on a novel generalized Cauchy-Rayleigh mixture

In this paper, a mixture of generalized Cauchy distribution and Rayleigh distribution that possesses a closed-form expression is proposed for modeling the heavy-tailed Rayleigh (HTR) distribution. This new approach is developed for analytically modeling the amplitude distribution of ultrasound images based on the HTR distribution. HTR as a non-Gaussian distribution is basically the amplitude probability density function (PDF) of the complex isotropic symmetric α-stable (S α S) distribution which appears in the envelope distribution of ultrasonic images. Analytic expression for HTR distribution is a momentous consideration in signal processing with stable random variables. Furthermore, we introduce a mixture ratio estimator based on the energy of amplitude PDF which contains both α and γ parameters. For a quantitative assessment, we compare the accuracy and computational complexity of the proposed mixture with other approximations of HTR distribution through several numeral simulations on synthetic random samples. Experimental results obtained from the Kolmogorov-Smirnov (K-S) distance and Kullback-Leibler (K-L) divergence as the goodness-of-fit tests on real ultrasound images reveal the favor of the new mixture model.

Reduction of speckle noise ultrasound images based on TV regularization and modified bayes shrink techniques

A challenge for the researchers in medical ultrasound images is speckle noise reduction. In our work, we introduced a new method which reduces speckle noise in ultrasound images. Our method is based on TV regularization and modified bayes shrink. First, TV regularization is performed on the ultrasound image. Then, we perform a discrete wavelet transform on the coefficients obtained from total variation. Some subbands involving an approximation subband and detail subbands are revealed in output. Afterward, modified bayes shrink and soft thresholding are performed on the coefficients of the detail subbands whereas keeping the coefficients of the approximation fixed. Finally, invert the wavelet transform to retrieve the image. The performance of the algorithm has been tested using quantitative measures on synthetic and real ultrasound images. Experiments show that the proposed method does remove speckle noise significantly.

Fractional order integration and fuzzy logic based filter for denoising of echocardiographic image

BackgroundUltrasound is widely used for imaging due to its cost effectiveness and safety feature. However, ultrasound images are inherently corrupted with speckle noise which severely affects the quality of these images and create difficulty for physicians in diagnosis. To get maximum benefit from ultrasound imaging, image denoising is an essential requirement. MethodTo perform image denoising, a two stage methodology using fuzzy weighted mean and fractional integration filter has been proposed in this research work. In stage-1, image pixels are processed by applying a 3 × 3 window around each pixel and fuzzy logic is used to assign weights to the pixels in each window, replacing central pixel of the window with weighted mean of all neighboring pixels present in the same window. Noise suppression is achieved by assigning weights to the pixels while preserving edges and other important features of an image. In stage-2, the resultant image is further improved by fractional order integration filter. ResultsEffectiveness of the proposed methodology has been analyzed for standard test images artificially corrupted with speckle noise and real ultrasound B-mode images. Results of the proposed technique have been compared with different state-of-the-art techniques including Lsmv, Wiener, Geometric filter, Bilateral, Non-local means, Wavelet, Perona et al., Total variation (TV), Global Adaptive Fractional Integral Algorithm (GAFIA) and Improved Fractional Order Differential (IFD) model. Comparison has been done on quantitative and qualitative basis. For quantitative analysis different metrics like Peak Signal to Noise Ratio (PSNR), Speckle Suppression Index (SSI), Structural Similarity (SSIM), Edge Preservation Index (β) and Correlation Coefficient (ρ) have been used. Simulations have been done using Matlab. Simulation results of artificially corrupted standard test images and two real Echocardiographic images reveal that the proposed method outperforms existing image denoising techniques reported in the literature. ConclusionThe proposed method for denoising of Echocardiographic images is effective in noise suppression/removal. It not only removes noise from an image but also preserves edges and other important structure.

A hybrid edge-based segmentation approach for ultrasound medical images

Ultrasound imaging is one of the most widely used and the cheapest diagnostic tools of medical imaging modalities. In this paper, a hybrid approach for accurate segmentation of the ultrasound medical images is presented that utilizes both the features of kernel fuzzy clustering with spatial constraints and edge based active contour method using distance regularized level set (DRLS) function. The result obtained from the kernel fuzzy clustering is utilized not only to initialize the curve that spreads to identify the estimated region or object boundaries, but also helps to estimate the optimal parameters, which are responsible for controlling the level set evolution. The DRLS formulation also increase the processing speed by removing the need of re-initialization of the level set function. The performance of the proposed method is evaluated by conducting the several experiments on both the synthetic and real ultrasound images. Experimental results show that the proposed method improves the segmentation accuracy and also produces better results by successfully segmenting the object boundaries compared to others.

Hybrid Strategy to Simulate 3-D Nonlinear Radio-Frequency Ultrasound Using a Variant Spatial PSF

There are several simulators for medical ultrasound (US) applications that can fully compute the nonlinear propagation on the transmitted pulse and the corresponding radio-frequency (RF) images. Creanuis is one recent model used to generate nonlinear RF images; however, the time requirements are long compared with linear models using a convolution strategy. In this paper, we describe an approach using convolution coupled with nonlinear information to create a pseudoacoustic tool that is able to quickly generate realistic US images. Several point-spread functions (PSFs) are computed with Creanuis. These PSFs are extracted at different depths in order to take into account variation in the resolution and apparition of harmonics during propagation. One convolution is then conducted for each PSF to generate a set of nonlinear raw RF images. The final image is obtained by merging these raw images using a PSF-weighting function. This hybrid Creanuis strategy was extended to 2-D, 2-D +t , 3-D, and 3-D +t images for both linear and phased-array geometries. We validated h-Creanuis using the mean deviation between the proposed images and those created using Creanuis and examined their statistical distributions. The mean deviations of Creanuis and h-Creanuis are below 2.5% for fundamental and second-harmonic images. The 3-D +t images obtained demonstrate the correct motion characteristics for speckle in sequences of both fundamental and second-harmonic images.

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.

Coupled PDE for Ultrasound Despeckling Using ENI Classification

Speckle is a type of noise which is often present in ultrasound images. Speckle is formed due to constructive or destructive interference of ultrasound waves. Due to the granular pattern of speckle noise, it hides important details in ultrasound images. Many despeckling techniques are proposed in the literature, but most of them fail to reach a balance between the removal of speckle noise and preservation of the fine details in the image. In this work, an improved coupled PDE model is proposed which combines second order selective degenerate diffusion (SDD) model and fourth order PDE model based on the assumption that speckle in ultrasound image follows Gamma distribution. An edge noise interior (ENI) method is used to control the diffusion. With the help of ENI controlling function, the diffusion at edge pixels and noisy pixels are selectively accomplished with varying speed. Thus, the proposed model preserves the edges and fine texture details in the image. The model is tested on simulated images after corrupting the images with various levels of Gamma noise. Further, we have tested it on real ultrasound images also. The performance of the proposed model is compared with other similar techniques and the proposed method outperforms other state-of-the-art methods, both in terms of qualitative and quantitative measures.