Speckle filtering of medical ultrasonic images using wavelet and guided filter

A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images

Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.

Measuring the Depth of Subsurface Defects in Additive Manufacturing Components by Laser-Generated Ultrasound

A new method to measure the depth of subsurface defects in additive manufacturing components is proposed based on the velocity dispersion analysis of Lamb waves by the wavelet-transform of laser ultrasound. Firstly, the mode-conversion from laser-generated surface waves to Lamb waves caused by subsurface defects at different depths is studied systematically. Secondly, an additive manufactured 316L stainless steel sample with six subsurface defects has been fabricated to validate the efficiency of the proposed method. The measured result of the defect depth is very close to the real designed value, with a fitting coefficient of 0.98. The defect depth range for high accuracy measurement is suggested to be lower than 0.8 mm, which is enough to meet the inspection of layer thickness during additive manufacturing. The result indicates that the proposed method based on laser-generated ultrasound (LGU) velocity dispersion analysis is robust and reliable for defect depth measurement and meaningful to improve the processing quality and processing efficiency of additive/subtractive hybrid manufacturing.

Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images. Numerous de-speckling schemes were established to date that concern speckle reduction; however, the models suffer from demerits like computational time, computational complexity, etc., that are to be rectified as soon as possible. This compulsion takes to the introduction of a new de-speckling model via an adaptive hybrid filter model that includes four filters like guided filter (GF), speckle-reducing bilateral filter (SRBF), rotation invariant bilateral nonlocal means filter (RIBNLM), and median filter (MF) respectively. Moreover, the novelty goes under the selection of optimal filter coefficients that make the process effective. Bayesian-based neural network is used to predict the appropriate filter coefficients, where the training library is constructed with the optimal coefficients. Along with this, the selection of optimal filter coefficients is done under the defined objective function using a new hybrid algorithm termed as Randomized FireFly (FF) update in Lion Algorithm (RFU-LA) that hybrids the concept of both LA and FF, respectively. Finally, the performance of the proposed de-speckling model is compared over that of other conventional models with respect to different performance measures. Accordingly, from the analysis, the mean MAPE of the proposed method are 39.13% and 49.28% higher than those of the wavelet filtering and hybrid filtering schemes for a noise variance of 0.1.

Ultrasonic signal enhancement for coarse grain materials by machine learning analysis

This paper aims at dealing with the dilemma of examining the existence of a defect in ultrasonic detection of coarse grain materials. In such cases, defect echoes can be drowned in a strong noise background resulting from intricate coarse grain scattering, that is, grain noise. To this end, we develop an innovative signal reconstruction methodology from polluted measurements which combines basic statistical analysis with a series of machine learning algorithms. The proposed methodology analyzes abundant information from numerous raw signals to distinguish the desired signal from grain noise, avoiding the limitation of information provided only by a single signal. The technique is achieved by collecting similar signals together through a clustering algorithm and subsequently inputting these similar signals to a denoising autoencoder to suppress the grain noise. It is successfully employed to ultrasonic signals obtained from an as-cast stainless steel specimen with coarse equiaxed grains, a stainless steel specimen with relatively homogeneous dendrite fabricated by additive manufacturing and a stainless steel weld with heterogeneous columnar grains having variation of grain sizes in various locations. The influence of material microstructure and probe frequency on denoising performance is investigated in detail. Based on this, the proposed methodology is applied to defect detection. Desired A-scan results and B-scan imaging are achieved by the proposed method, where defects are well revealed. The experimental results demonstrate the developed methodology has stable excellent performance and superior denoising capabilities for defect detection with respect to conventional techniques, especially in the case where the noise is almost the same as the desired signal.

Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

Anisotropic Diffusion Based Multiplicative Speckle Noise Removal

Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.

Medical Image Enhancement by a Bilateral Filter Using Optimization Technique

For researchers, denoising of Magnetic Resonance (MR) image is a greatest challenge in digital image processing. In this paper, the impulse noise and Rician noise in the medical MR images are removed by using Bilateral Filter (BF). The novel approaches are presented in this paper; Enhanced grasshopper optimization algorithm (EGOA) is used to optimize the BF parameters. To simulate the medical MR images (with different variances), the impulse and Rician noises are added. The EGOA is applied to the noisy image in searching regions of window size, spatial and intensity domain to obtain the filter parameters optimally. The PSNR is taken as fitness value for optimization. We examined the proposed technique results with other MR images After the optimal parameters assurance. In order to comprehend the BF parameters selection importance, the results of proposed denoising method is contrasted with other previously used BFs, genetic algorithm (GA), gravitational search algorithm (GSA) using the quality metrics such as signal-to-noise ratio (SNR), structural similarity index metric (SSIM), mean squared error (MSE), and PSNR. The outcome shows that the EOGA method with BF shows good results than the earlier methods in both edge preservation and noise elimination from medical MR images. The experimental results demonstrate the performance of the proposed method with the accuracy, computational time, and maximum deviation, Peak Signal to Noise Ratio (PSNR), MSE, SSIM, and entropy values of MR images over the existing methods.

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold

Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).

A Motion Artifact Reduction Method in Cerebrovascular DSA Sequence Images

Digital Subtraction Angiography (DSA) can be used for diagnosing the pathologies of vascular system including systemic vascular disease, coronary heart disease, arrhythmia, valvular disease and congenital heart disease. Previous studies have provided some image enhancement algorithms for DSA images. However, these studies are not suitable for automated processes in huge amounts of data. Furthermore, few algorithms solved the problems of image contrast corruption after artifact removal. In this paper, we propose a fully automatic method for cerebrovascular DSA sequence images artifact removal based on rigid registration and guided filter. The guided filtering method is applied to fuse the original DSA image and registered DSA image, the results of which preserve clear vessel boundary from the original DSA image and remove the artifacts by the registered procedure. The experimental evaluation with 40 DSA sequence images shows that the proposed method increases the contrast index by 24.1% for improving the quality of DSA images compared with other image enhancement methods, and can be implemented as a fully automatic procedure.

Image Segmentation Based on Statistical Confidence Intervals

Image segmentation is defined as a partition realized to an image into homogeneous regions to modify it into something that is more meaningful and softer to examine. Although several segmentation approaches have been proposed recently, in this paper, we develop a new image segmentation method based on the statistical confidence interval tool along with the well-known Otsu algorithm. According to our numerical experiments, our method has a dissimilar performance in comparison to the standard Otsu algorithm to specially process images with speckle noise perturbation. Actually, the effect of the speckle noise entropy is almost filtered out by our algorithm. Furthermore, our approach is validated by employing some image samples.

A hybrid algorithm for speckle noise reduction of ultrasound images

BACKGROUND AND OBJECTIVE

Medical 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

Solder joint failure localization of welded joint based on acoustic emission beamforming

A localization approach of welded joint damage is proposed based on acoustic emission (AE) beamforming. In this method, a uniform line array is introduced to detect the AE signal of welded joints in specified area. In order to investigate the influence of fillet and crimping commonly existing in a welded plate structure during the AE wave propagation process, the finite element method (FEM) is applied to simulate the behavior of AE wave in the specimen. The simulation localization results indicate that the proposed localization approach can effectively localize AE sources although there exist the fillet and crimping, and it is also validated by the pencil-lead-broken test on rectangular steel tube with welded joints. Finally, the proposed method is adopted to localize the failure of solder joint in operation vibration condition. The proposed method is successful to localize the compact AE source caused by the cracked joint based on wavelet packet transform.

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal

BACKGROUND

In breast ultrasound elastography, tissues displacements estimation is obtained through a technique that follows the evolution of tissues under stress. However, during the acquisition of B-mode images, tissue displacements are often contaminated with multiplicative noise caused by changes in the speckle pattern in the tissue. Thus, the application of monogenic signal technique on the B-mode image in order to estimate displacement tissue, result in a presence of amplified noise in the deformation tissue image, which severely obscures the useful information. In this paper, we propose a new method based on the monogenic features, that is to improve the old monogenic signal (OMS) technique by improving the filtering step, so that the use of an effective denoising technique is enough to ensure a good estimation of displacement tissue. Our proposed method is based on the use of a robust filtering technique combined with the monogenic model.

METHODS

Two models of phantom elasticity are used in our test validation sold by CIRS company. In-vivo testing was also performed on the sets of clinical B-mode images to 20 patients including malignant breast tumors. Shrinkage wavelets has been used to eliminate the noise according to the threshold, then a guided filter is introduced to completely filter the image, the monogenic model is used after excerpting the image feature and estimating analytically the displacement tissue.

RESULTS

Accurate and excellent displacement estimation for breast tissue was observed in proposed method results. By adapting our proposed approach to breast B-mode images, we have shown that it demonstrated a higher performance for displacement estimation; it gives better values in term of standard deviation, higher contrast to noise ratio, greater peak signal-to-noise ratio, excellent structural similarity and much faster speed than OMS and B-spline techniques. The results of the proposed model are encouraging, allowing quick and reliable estimations.

CONCLUSION

Although the proposed approach is used in ultrasound domains, it has never been used in the estimation of the breast tissue displacement. In this context, our proposed approach could be a powerful diagnostic tool to be used in breast displacement estimation in ultrasound elastography.