Speckle Reduction In Ultrasound Images Research Articles

An adaptive total generalized variational model for speckle reduction in ultrasound images

In this paper, we propose an adaptive total generalized variation model for removing the speckle noise in ultrasound image. The existence and uniqueness of the minimizers to the proposed model are established. Furthermore, we design an alternating minimization algorithm to solve numerically the model. By incorporating the generalized cross validation technique, the regularization parameter of the model can be updated during the iterations of the designed algorithm. Finally, experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with that of the other testing methods.

FDDL-Net: frequency domain decomposition learning for speckle reduction in ultrasound images

FDDL-Net: frequency domain decomposition learning for speckle reduction in ultrasound images

Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation.

Designing a technique with higher speckle noise suppressing capability, better edge preserving performance, and lower time complexity is a research objective for the common carotid artery (CCA) ultrasound despeckling. Total variation based techniques have been widely used in the image denoising and have good performance in preserving the edges in the images. However, the total variation based filters can produce the staircase artifacts. To address this issue, second-order total variation based techniques have been proposed for the image denoising. However, the previous study has been proved that the fractional differential model has better performance in reducing the speckles in ultrasound despeckling compared with the second-order model. Thus, to improve the performance of ultrasound despeckling and edge preserving, a novel despeckling model based on integer and fractional-order total variation (IFOTV) is proposed for CCA ultrasound images. Moreover, the minimization problems in our despeckling model are solved by the alternating direction method of multiplier (ADMM). In results with synthetic images, the edge preservation index (EPI) values of proposed method are 0.9524, 0.8797, and 0.7351 as well as 0.9137, 0.8253, and 0.6847 under three different levels of noise, which are the highest among four advanced methods. In results with simulated CCA ultrasound images, the speckle suppression and mean preservation indices of proposed method are 0.5596, 0.6571, and 0.8106 under three different levels of noise, which are the best among four advanced methods. In results with clinical images, the average absolute error of intima-media thickness measurements of proposed method is 0.0660 ± 0.0679 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the IFOTV method has improved performance in suppressing the speckle noise and preserving the edge, and is thus a potential alternative to the current filters for the CCA ultrasound despeckling.

BEMD Based Cross Bilateral Filtering Technique for Speckle Reduction in Ultrasound Images

In this paper, Bidimensional Empirical Mode Decomposition (BEMD) based Cross Bilateral Filter (CBF) technique for speckle reduction in ultrasound images has been proposed. The reference image is obtained by denoising the noisy image using pixel-wise Wiener filtering. Then, both the noisy image and the reference image are decomposed into a set of Intrinsic Mode Functions (IMFs) and the residue image using BEMD technique. CBF is applied between noisy image IMFs and the corresponding reference image IMFs. The image is reconstructed back with these modified IMFs and the residue. The proposed method exploits the edge information in the reference image for improving the quality of the denoised image. The performance of the proposed method has been tested for real ultrasound images and simulated images having noise of different variance. The experimental results show that the proposed algorithm performs better than other state-of-art methods in terms of Edge Keeping Index (EKI), Correlation Coefficient (CC), Figure of Merit (FOM), Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) for synthetic images. The algorithm gives better performance for real ultrasound images in terms of Mean to Variance Ratio (MVR) and Equivalent Number of Looks (ENL).

A Weibull‐distribution‐based hybrid total variation method for speckle reduction in ultrasound images

Speckle reduction is still an intractable task in ultrasound imaging field. Ultrasound speckle is usually described as multiplicative noise with its statistics following a Rayleigh or Gaussian distribution. To employ these two distributions effectively, the authors attempt to describe ultrasound speckle using a Weibull distribution, because it can include the Rayleigh distribution as a special case and also approximate a Gaussian distribution by varying its shape and scale parameters. The authors’ contribution in this paper is to propose a Weibull-distribution-based hybrid total variation (WHTV) method to reduce ultrasound speckle. The WHTV energy functional is convex and consists of a new data fidelity term and a new regularization term. The former is derived from the multiplicative Weibull model of ultrasound speckle based on the maximum likelihood criterion. The latter is a new edge-weighted combination of the first- and second-order total variation, with the advantage of preserving edges while alleviating the staircase effects. The minimization of the WHTV energy functional is implemented by the split Bregman algorithm. Experimental results on synthetic and real ultrasound images have demonstrated not only that the Weibull distribution is a better fitting model for the statistics of ultrasound speckle than other distributions such as Rayleigh, Gaussian, Gamma, and Nakagami, but also that the proposed WHTV method can achieve better despeckling performance than several state-of-the-art variational methods.

Hybridization of Adaptive Wavelet Shrinkage with Guided Filter for Speckle Reduction in Ultrasound Images

Speckle noise is introduced in ultrasound images during acquisition and it is inherited in all coherent sources. Speckle makes area of interest difficult to study for human observer and for automated diagnostic machines hence the interpretation may adversely affect the results. Numerous post processing de-speckling algorithms have been developed but along with speckle reduction the challenging task is to preserve edges or evincive detail. In this study, authors have developed a hybrid method using adaptive wavelet shrinkage and guided filter which has given us remarkable results in de-speckling and edge preservation. In the paper proposed method is compared with existing filters like Lee, Kuan, Frost, Guided filter and wavelet shrinkage techniques Visu Shrink, Bayes Shrink and Normal Shrink. The results of proposed technique were visually better than existing de-speckling techniques. The quantitative analysis also shows eminence of proposed technique over existing techniques when compared using different image quality metrics.

Second order total generalized variation for speckle reduction in ultrasound images

Image denoising is one of the most important issues in image processing. For removing the speckle noise in ultrasound images, researchers have proposed the minimization models based on the total variation (TV), which effectively preserve the sharp edges. But they simultaneously suffer form the undesired artifacts, such as the staircase effect. To overcome this shortcoming, we propose a convex model by combining with the total generalized variation (TGV) regularization for retaining the fine detail and reducing the staircase effect. Furthermore, we develop an alternating direction method of multiplier (ADMM) to solve the proposed model. Experimental results demonstrate that our model outperforms some state-of-the-art methods in terms of visual and quantitative measures.

An Efficient Filtering Approach for Speckle Reduction in Ultrasound Images

Introduction Medical Ultrasound (US) Imaging technique is a non-ionizing radiations imaging modality that enables real time diagnosis treatment. This technique has non-invasive nature. It is widely used in medical field for diagnosis, patient routine check-ups for good health, and more and more u

Gradient Controlled Adaptive Non-Local Means Method for Speckle Reduction in Ultrasound Images

Gradient Controlled Adaptive Non-Local Means Method for Speckle Reduction in Ultrasound Images

An analysis of two variational models for speckle reduction of ultrasound images

In this paper, we consider two variational models for speckle reduction of ultrasound images. By employing the G-convergence argument we show that the solution of the SO model coincides with the minimizer of the JY model. Furthermore, we incorporate the split Bregman technique to propose a fast alterative algorithm to solve the JY model. Some numerical experiments are presented to illustrate the efficiency of the proposed algorithm.

Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation

Abstract Speckle reduction is an important pre-processing stage for ultrasound medical image processing. In this paper, an adaptive fuzzy logic approach for speckle noise reduction in ultrasound images is presented. In the proposed method, adaptiveness is incorporated at two levels. In the first level, applying fuzzy logic on the coefficients of variation computed from the noisy image, image regions are classified. The best suitable filter for the particular image region is adaptively selected by the system yielding appreciable improvement in noise suppression and preservation of image structural details. At the second level, to distinguish between edges and noise, the proposed method uses a weighted averaging filter. The structural similarity measure, which depends on the nature of image and quantity of noise present in the image, is used as the tuning parameter. Thus with two levels of adaptiveness, the proposed method has better edge preservation compared to existing methods. Experimental results of the proposed method for natural images, Field II simulated images and real ultrasound images, show that proposed denoising algorithm has better noise suppression and is able to preserve edges and image structural details compared with existing methods.

Nonlocal Total-Variation-Based Speckle Filtering for Ultrasound Images.

Ultrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balance between speckle reduction and edge preservation. In this paper, an adaptation of the nonlocal total variation (NLTV) filter is proposed for speckle reduction in ultrasound images. The speckle is modeled via a signal-dependent noise distribution for the log-compressed ultrasound images. Instead of the Euclidian distance, the statistical Pearson distance is introduced in this study for the similarity calculation between image patches via the Bayesian framework. And the Split-Bregman fast algorithm is used to solve the adapted NLTV despeckling functional. Experimental results on synthetic and clinical ultrasound images and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both speckle noise reduction and tissue boundary preservation for ultrasound images.

Detection of Kidney Abnormalities in Noisy Ultrasound Images

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its non-invasive nature, low cost and capability of forming real time imaging. Ultrasound imaging uses high-frequency sound to image internal structures by differing reflection signals produced when a beam of sound is projected into the body and bounces back at interfaces between the structures. However the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. The speckle pattern depends on the structure of the image tissue and various imaging parameters. Speckle noise present in ultrasound image affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. There are two main purposes for speckle reduction in medical ultrasound imaging (1) to improve the human interpretation of ultrasound images (2) despeckling is the preprocessing step for many ultrasound image processing tasks such as segmentation and registration. A number of methods have been proposed for speckle reduction in ultrasound imaging.

A Suitable Threshold for Speckle Reduction in Ultrasound Images

This paper presents a novel parametric thresholding procedure to reduce the effect of speckle noise in ultrasound (US) medical images. The method comprises the use of an adaptive data-driven exponential operator that operates on wavelet coefficients of the US image to suppress undesired effects of disturbances, preserving signal details. The obtained results demonstrate that the proposed denoising method increases the medical image quality and, therefore, it can be a useful tool in medical diagnosis.

Despeckling of Ultrasound Medical Images: A Survey

Digital image acquisition and processing techniques plays important role in clinical diagnosis. Medical images are generally corrupted by noise during their acquisition and transmission. Removing noise from the original medical image is still a challenging problem for researchers. Ultrasound imaging is widely used for diagnosis over the other imaging modalities like Positron Emission Tomography (PET), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) due to its noninvasive nature, portable, accurate, harmless to the human beings and capability of forming real time imaging. The presence of signal dependant noise known as speckle degrades the usefulness of ultrasound imaging. The main purpose for speckle reduction is to improve the visualization of the image and it is the preprocessing step for segmentation, feature extraction and registration. Over a period, a number of methods have been proposed for speckle reduction in ultrasound imaging. While using techniques for speckle reduction as an aid for visualization, certain speckle contains diagnostic information and should be retained. The scope of this paper is to give an overview about despeckling techniques in ultrasound medical imaging. 

Denoising of Medical Ultrasound Images Using Spatial Filtering and Multiscale Transforms

Medical imaging became the integral part in health care where all the critical diagnosis such as blocks in the veins, plaques in the carotid arteries, minute fractures in the bones, blood flow in the brain etc are carried out without opening the patient’s body. There are various imaging modalities for different applications to observe the anatomical and physiological conditions of the patient.These modalities will introduce noise and artifacts during medical imag e acquisition. If the noise and artifacts are not minimised diagnosis will become difficult.One of the non-invasive modality widely used is ultrasound Imaging where no question of radiation but suffers from speckle noise produced by the small particles in the tiss ues who’s size is less than the wavelength of the ultrasound. The presence of the speckle noise will cause the low contrast images because of this the low contrast lesions and tumours can’t be detected in the diagnostic phase. So there is a strong need in developing the despeckling techniques to improve the quality of ultrasound images. Here in this paper we are presenting the denoising techniques for speckle reduction in ultrasound imaging. First we presented the various spatial filters and their suitabil ity for reducing the speckle. Then we developed the denoising methods using multiscale transforms such as Discrete Wavelet Transform (DWT), Undecimated Discrete Wavelet Transform (UDWT) , dual tree complex wavelet transform (DTCDWT) and Double density dualtree complex wavelet transform (DDDTCDWT). The performance of the filters was evaluated using various metrics based on pixel based, correlation based, edge based and Human visual system (HVS) based and we found that denoising using double density dual tree complex discrete wavelet transform is outperformed with best edge preserving feature.

A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images

BackgroundSpeckles in ultrasound imaging affect image quality and can make the post-processing difficult. Speckle reduction technologies have been employed for removing speckles for some time. One of the effective speckle reduction technologies is anisotropic diffusion. Anisotropic diffusion technology can remove the speckles effectively while preserving the edges of the image and thus has drawn great attention from image processing scientists. However, the proposed methods in the past have different disadvantages, such as being sensitive to the number of iterations or low capability of preserving the details of the ultrasound images. Thus a detail preserved anisotropic diffusion speckle reduction with less sensitive to the number of iterations is needed. This paper aims to develop this kind of technologies.ResultsIn this paper, we propose a robust detail preserving anisotropic diffusion filter (RDPAD) for speckle reduction. In order to get robust diffusion, the proposed method integrates Tukey error norm function into the detail preserving anisotropic diffusion filter (DPAD) developed recently. The proposed method could prohibit over-diffusion and thus is less sensitive to the number of iterationsConclusionsThe proposed anisotropic diffusion can preserve the important structure information of the original image while reducing speckles. It is also less sensitive to the number of iterations. Experimental results on real ultrasound images show the effectiveness of the proposed anisotropic diffusion filter.

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.

New Wavelet-Based Algorithm for Speckle Reduction in Ultrasound Images

This paper proposes an adaptive threshold estimation method for de-noising in wavelet domains merged with translation invariant de-noising. The sub-band shrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on subband data. A new probability density function is proposed to model the statistics of wavelet coefficients. The subband threshold is derived using Bayesian estimation theory and the new pdf. Different shifts are used and applied to the noisy image in order to attain different estimates to the unknown image and then linearly average the estimates. In speckle images, the noise content is multiplicative. The proposed method is applied for speckle ultrasound images by using logarithmic transformation. Experimental results on several test images are compared with various de-noising techniques.

10.1007/s11493-008-1008-1

Speckle can be described as random multiplicative noise. It hampers the perception and extraction of fine details in the image. Speckle reduction techniques are applied to ultrasound images in order to reduce the noise level and improve the visual quality for better diagnoses. It is also used as preliminary treatment before segmentation and classification. Several methods have been proposed for speckle reduction in ultrasound images. Multiscale contrast enhancement has proven to be very efficient for x-ray images. A recent study by Dippel et al. doing a comparison, contrast enhancement of radiographs (x-ray and mammography), between the Laplacian pyramid and the wavelet one proves that the Laplacian pyramid method gives a better result than the wavelet one; the filtering aspect was not taken into account. In ultrasound images a strong contrast variation exists which is different from x-ray and mammography. In this paper a wavelet pyramid with simultaneous speckle reduction and contrast enhancement was applied for the first time on ultrasound images with the area of interest and compared to a Laplacian enhancement pyramid. The optimum choice of wavelet bases for ultrasound images is investigated in this study. In order to realize a fair comparison, the same nonlinear modification in both multiscale schemes is used. The comparison proves that the wavelet pyramid gives a much better result than the Laplacian one for simultaneous speckle reduction and contrast enhancement of ultrasound images.