Image Speckle Reduction Research Articles

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Role of Renal Sonography in the Diagnosis of Chronic Kidney Disease

Aim: to use sonographic imaging in grading CKD and assess the serum creatinine, renal longitudinal size,parenchymal thickness and compare these parameters based upon ultrasonographic grade.Material and Methods: Present cross-sectional; study was conducted at the Department of Radiology, Tertiary Care Teaching Institute of India for the duration of 1 year. There are 120 participants with CKD in the research.All patients received a questionnaire, and a thorough clinical examination was done. Each participant underwent sector curved array transducer of 3.5–5 MHz ultrasound images of their liver and kidneys. Low tissue harmonic and speckle reduction imaging techniques are used to measure the echogenicity of the kidney and liver while reducing interobserver bias. In all the participants, the mean values of both the kidneys renal longitudinal size and parenchymal thickness were calculated. Renal cortical echogenicity was compared and graded with the echogenicity of the liver and renal medulla.Results: The mean age of the CKD patients was found to be 45.23 ± 06.05 years. It was found that 50 patientshad Grade 1, 40 patients had Grade 2, 20 patients had Grade 3 and 10 patients had Grade 4. The results showed that, when the comparison was done, serum creatinine was found to be significant among echogenicity grades with ANOVA. when the comparison was done; the mean longitudinal size was found to be significant among echogenicity. On comparison was found to be mean Parenchymal thickness was found to be significant among echogenicity grades.Conclusion: Renal cortical echogenicity and its grading in relation to longitudinal length, parenchymal thickness, and cortical thickness in CKD patients are the best sonographic parameters that correlate with blood creatinine. It is possible to use renal cortical echogenicity as a metric of renal function since it has the benefit of being irreversible in compared to serum creatinine levels.

Technical innovations to optimize ultrasound imaging

Due to the high incidence of thyroid disease, ultrasound examination of the neck has high priority in many nuclear medicine departments. A precise sonogram with high spatial resolution and image sharpness over the entire imaging area, combined with minimal artifacts, is the prerequisite for meeting the demand for high diagnostic accuracy in modern medicine. In the last 20-30 years, a number of significant technical innovations have been implemented, such as the development of the matrix array, electronic image focusing, realtime compound imaging, artifact limitation by speckle reduction and harmonic imaging, as well as the possibility to extend the field of view.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.

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).

Kernel PCA Based Non-Local Means Method for Speckle Reduction in Medical Ultrasound Images

Kernel PCA Based Non-Local Means Method for Speckle Reduction in Medical Ultrasound Images

Surface imaging of breast implants using modern high-frequency ultrasound technology in comparison to high-end sonography with power analyses for B-scan optimization1.

This study aims to evaluate optimized breast implant surface-structure analysis by comparing high-end ultrasound technology with a new high frequency technique. This comparative study used new breast implants with different surfaces in an in vitro setting. Nine idle silicon or polyurethane (PU) breast implants were examined by two investigators in an experimental in vitro study using two high-end ultrasound devices with multi-frequency transducers (6-15 MHz, 9-16 MHz, 12.5-33 MHz).The ultrasound B-Mode was optimized using tissue harmonic imaging (THI), speckle reduction imaging (SRI, level 0-5), cross beam (high, medium, low) and photopic.Using a standardized ultrasound protocol, the implants were examined in the middle (point of highest projection) and lateral, by two independent examiners.Image evaluation was performed on anonymized digital images in the PACS. The aim was to achieve an artifact-free recording of the surface structure, the surface coating, the total image structures and, as far as possible, an artifact-free internal representation of the implants.For independent surface evaluation a score was used (0 = undetectability of surface structures, rich in artifacts, 5 = best possible, artifact free image quality). The quality of ultrasound imaging of breast implant surfaces after the optimization of B-Scan differed significantly comparing high-end ultrasound technology with modern high-frequency ultrasound technology (p < 0,05).The following setting has been found to be the best setting with the highest image quality:B-Mode, SRI value 3, Crossbeam high level with color coded imaging for B- mode. In the total examined frequency range of 6-33 MHz, the highest image quality was found in the average frequency range of 12.5-33 MHz at both measured points. For both devices, device 1 (high-end) and device 2 (high frequency) ultrasound, the image quality was in the12.5-33 MHz frequency range with an average image quality of 3.236. It was significantly higher, than in the lower frequency ranges and the same frequency range with THI. (p < 0,05). The image quality of the high-end sonography device was superior to the conventional high-frequency ultrasound device in all frequency ranges. High-end ultrasound imaging technology was superior in the quality of implant surface evaluation in comparison to high-frequency ultrasound sonography. The gained knowledge can serve as a basis for further multicenter clinical application and studies with the aim to develop an objective, precise tool to evaluate the implant and the surrounding tissue with ultrasound.

Laplacian pyramid based non-linear coherence diffusion for real-time ultrasound image speckle reduction

The inherent speckle noise in medical ultrasound image degrades valuable clinical information and affects the diagnosis quality. Therefore, commercial ultrasound systems always face a big challenge to improve the visual quality of ultrasound images by reducing the speckle noise. A new hybrid algorithm is proposed in the present paper for speckle reduction by combining nonlinear coherence diffusion (NCD) with pyramid-transform-based multi-scale filters (named as Laplacian Pyramid Based Non-linear Coherence Diffusion; viz. LPNCD). The new approach exhibits good capability in preserving tissue features while enhancing the coherence. The theory and implementation of the approach are presented and verified based on phantom and clinical data. By comparing with previous techniques on quantified signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), the results suggest that the LPNCD method may demonstrate superior performance than other commonly used AD-class filters. Meanwhile, the performance of GPU implementations of the current technique is also evaluated, and it is verified that the LPNCD approach may have good performance efficiency and wonderful visual effect as running on mainstream commercial ultrasound systems.

A Novel Optimized Method for Speckle Reduction in Medical Ultrasound Images

A Novel Optimized Method for Speckle Reduction in Medical Ultrasound Images

Speckle-Free SAR Image Ship Detection.

Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images.

Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at: https://github.com/tsmotlp/DRGAN-OCT.

Reference variance and adjustment factor based wave atoms image speckle reduction method for optical coherence tomography images

Speckle noise in optical coherence tomography (OCT) images causes blurring of small targets and structural edges in the image, which reduces the quality of OCT images. To address this problem, a reference variance and adjustment factor based wave atoms image speckle reduction method for OCT images is proposed. First, reference image and adjustment factor were introduced, and reference image speckle noise reference variance was adjusted using adjustment factor to obtain the target image speckle noise variance. Then, a wave atoms threshold equation including the reference variance and the adjustment factor was constructed to obtain an accurate wave atoms noise reduction threshold. On this basis, speckle noise was shrunk using the wave atoms noise reduction threshold to achieve speckle noise reduction in OCT images, and OCT images with clear small targets and sharp structure edges were obtained. The effectiveness and advantages of the proposed method were illustrated with human eye OCT image and retinal OCT image.

SAR Image Speckle Reduction Based on Nonconvex Hybrid Total Variation Model

Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher-Tippett (FT) distribution-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method.

Techniques for Improving Ultrasound Visualization of Biopsy Markers in Axillary Lymph Nodes.

Objective:Biopsy markers are often placed into biopsy-proven metastatic axillary lymph nodes to ensure later accurate node excision. Ultrasound is the preferred imaging modality in the axilla. However, sonographic identification of biopsy markers after neoadjuvant therapy can be challenging. This is due to poor conspicuity relative to surrounding parenchymal interfaces, treatment-related alteration of malignant morphology during neoadjuvant chemotherapy, or extrusion of the marker from the target. To the authors’ knowledge, the literature provides no recommendations for ultrasound scanning parameters that improve the detection of biopsy markers. The purpose of this manuscript is 3-fold: (1) To determine scanning parameters that improve sonographic conspicuity of biopsy markers in a phantom and cadaver model; (2) to implement these scanning parameters in the clinical setting; and (3) to provide strategies that might increase the likelihood of successful ultrasound detection of biopsy markers in breast imaging practices.Materials and Methods:An ex vivo study was performed using a phantom designed to simulate the heterogeneity of normal mammary or axillary soft tissues. A selection of available biopsy markers was deployed into this phantom and ultrasound (GE LOGIQ E9) was performed. Scanning parameters were adjusted to optimize marker conspicuity. For the cadaver study, the biopsy markers were deployed using ultrasound guidance into axillary lymph nodes of a female cadaver. Adjustments in transducer frequency, dynamic range, cross-beam (spatial compound imaging), beam steering, speckle reduction imaging, harmonic imaging, colorization, and speed of sound were evaluated. Settings that improved marker detection were used clinically for a year.Results:Sonographic scanning settings that improved biopsy marker conspicuity included increasing transducer frequency, decreasing dynamic range, setting cross-beam to medium hybrid, turning on beam steering, and setting speckle reduction imaging in the mid-range. There was no appreciable improvement with harmonic imaging, colorization, or speed of sound.Conclusion:On a currently available clinical ultrasound scanning system, ultrasound scanning parameters can be adjusted to improve the conspicuity of biopsy markers. Overall, optimization requires a balance between techniques that clinically increase contrast (dynamic range, harmonic imaging, and steering) and those that minimize graininess (spatial compound imaging, speckle reduction imaging, and steering). Additional scanning and procedural strategies have been provided to improve the confidence of sonographic detection of biopsy markers closely associated with the intended target.

Laser speckle reduction using spatially structured and temporally varying beams using double diffractive optical elements

We report a compact, low cost, multi-color diode laser based illumination system for display and laser-projectors. An innovative speckle reduction system based on spatially structured and temporally varying beams was designed and developed. Three primary color lasers were efficiently combined and then made incident onto electromechanically controlled diffractive optical elements. This system splits a single laser beam to multiple moving beams, thus realizing the spatial, angular and temporal diversity simultaneously. Multiple moving beams are made incident onto a large diameter plano-convex lens. Thus, collecting most of the scattered light and focused on the input of the multimode fiber bundle (MMFB). A compact electrical vibrator was developed to vibrate MMFB. The entire system was realized for speckle reduction significantly with the combined effect of spatial, temporal, angular and wavelength diversity. Experimental results of speckle reduction and speckle free imaging are presented. We found that with our system the laser speckle contrast was reduced from 0.65 to 0.02 which is an order of magnitude speckle reduction. CIE color coordinates (x, y ) value of this white light source was (0.333, 0.332) and CCT was 5461 K. RGB colour ratio was 75.3: 23.3: 1.4. The developed system may find application for speckle free projection imaging, quantitative phase microscopy and holography.

Speckle Suppression in Multi-Channel Coherent Imaging: A Tractable Bayesian Approach

Coherent imaging methods like synthetic aperture radar (SAR) are subject to speckle, and the suppression of this noise-like quality is often considered a prerequisite to image interpretation. Likewise, circumstances such as frequency jamming or system multiplexing produce gaps in the data and corresponding image artifacts, which further impact the utility of resulting imagery and induce correlation in the speckle process. For multi-channel SAR configurations such as the polarimetric or interferometric modes, a Bayesian imaging procedure is proposed to harvest the benefits of jointly processing channels in order to recover scattering information. The inference technique recovers the per-pixel multi-channel SAR covariance and incorporates a statistical model of speckle and a priori knowledge of the varieties of clutter present in the scene. Further, by combining speckle reduction into the image formation process for gapped aperture measurements, the estimator can balance competing objectives of image resolution, side-lobe suppression, texture preservation, and speckle reduction. An expectation-maximization algorithm is made computationally tractable by a graph-coloring probing technique to provide the block-diagonal portion of a large matrix inverse. Recovery results with simulated and measured fully polarimetric airborne SAR data indicate that the proposed method reduces the appearance of speckle and preserves spatial resolution.

An Optimized Hybrid Filter designing for Speckle and Gaussian Noise Reduction in Ultra-Sound Images

Image which is a visual perception of a scene or something is a set of pixels that have certain values which appear in the form of colors to view that particular set of pixel as image. Image contains information about whatever is being depicted in the picture and hence it can be said as a useful source for storing or conveying information. Image noise is apparent in image region with low signal level, such as shadow region or under exposed images. The work presents the concept of noising and denoising in a digital image. Noise is a kind of disturbance that occurs in the channel at the time of transmission. Image denoising is the procedure of improving true images from the noisy images. For ages researchers have been proposing several techniques that were used to remove the noise from the image. Image denoising is the procedure of improving true image from the noisy image. At the time of such process it is difficult to reduce noise. Owing to this difficulty, numerous denoising models have been proposed [4]. The paper presents the reduction of speckle and Gaussian noise in the biomedical ultrasound images. In the proposed work, the Butterworth filter is applied for filtering the noisy image and then the coefficients are optimized by using the firefly algorithm mechanism to remove noise that occurs at the time of transmission and then it is hybrid with the Weiner filter. Experiments have been performed to check the performance of the proposed technique. The results are analyzed quantatively using PSNR and SSIM. The results are also evaluated on other performance parameters such as BER, MSE and fitness on speckle as well as on Gaussian noise.

A Novel Neutrosophic Method for Automatic Seed Point Selection in Thyroid Nodule Images.

The thyroid nodule is one of the endocrine issues caused by an irregular cell development. This rate of survival can be improved by earlier nodule detection. Accordingly, the accurate recognition of the nodule is of the utmost importance in providing powerful results in building the survival rate. The reduction in the accuracy of manual or semiautomatic segmentation methods for thyroid nodule detection is due to many factors, basically, the lack of experience of the sonographer and latency of operation. Most lesion regions in ultrasound images are homogeneous. Therefore, the value of entropy in these regions is high compared to its neighbours. Based on this criterion, a novel procedure for automatically selecting the seed point in thyroid nodule images is proposed. The proposed system consists of three components: neutrosophic image enhancement and speckle reduction to reduce speckle noise and automatic seed selection algorithm extracted from the centre of candidate block in ultrasound thyroid images based on the principle that most of its Higher Order Spectra Entropies (HOSE) from Radon Transform (RT) at different angles are within the range between average and maximum entropies, and the region growing image segmentation is applied with the constant threshold. The performance of proposed automatic segmentation method is compared with other methods in terms of calculating, True Positive (TP) value (96.44 ± 3.01%), False Positive (FP) value (3.55 ± 1.45%), Dice Coefficient (DC) value (92.24 ± 6.47%), Similarity Index (SI) (80.57 ± 1.06%), and Hausdroff Distance (HD) (0.42 ± 0.24 pixels). The proposed system can be considered as an added value to the malignancy diagnosis in thyroid nodule by an endocrinologist.

Speckle Reduction in Synthetic Aperture Radar Images in Wavelet Domain Using Laplace Distribution

Speckle Reduction in Synthetic Aperture Radar Images in Wavelet Domain Using Laplace Distribution