Approach For Speckle Reduction Research Articles

Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.

Aperture phase modulation with adaptive optics: a novel approach for speckle reduction and structure extraction in optical coherence tomography.

Speckle is an inevitable consequence of the use of coherent light in imaging and acts as noise that corrupts image formation in most applications. Optical coherence tomographic imaging, as a technique employing coherence time gating, suffers from speckle. We present here a novel method of suppressing speckle noise intrinsically compatible with adaptive optics (AO) for confocal coherent imaging: modulation of the phase in the system pupil aperture with a segmented deformable mirror (DM) to introduce minor perturbations in the point spread function. This approach creates uncorrelated speckle patterns in a series of images, enabling averaging to suppress speckle noise while maintaining structural detail. A method is presented that efficiently determines the optimal range of modulation of DM segments relative to their AO-optimized position so that speckle noise is reduced while image resolution and signal strength are preserved. The method is active and independent of sample properties. Its effectiveness and efficiency are quantified and demonstrated by both ex vivo non-biological and in vivo biological applications.

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

Approaches to laser speckle reduction for uniform illumination of the field of view of a microscope during biophysical studies

A comparative study of various approaches to speckle reduction showed that the use of a liquid crystal-based speckle reducer did not allow complete elimination of speckles. The use of mechanical devices for blurring of the speckle pattern in the field of view turned out to be more efficient in the case of quantitative luminescent microscopy. Virtually complete reduction of speckles was observed when a device that combines a ring-shaped fiber-optic light source and a vibration unit that shifts the butt-ends of optical fibers relative to the laser diode during measurement was used. This speckle-reduction approach was successfully used in microarray analysis.

A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images

<p>In this paper, we present a novel approach for joint decorrelation<br />and despeckling of synthetic aperture radar (SAR) imagery. An iterative<br />maximum a posterior estimation is performed to obtain the<br />correlation and speckle-free SAR data, which incorporates a correlation<br />model which realistically explores the physical correlated<br />process of speckle noise on signal in SAR imaging. The correlation<br />model is determined automatically via Bayesian estimation in the<br />log-Fourier domain and patch-wise computation is used to account<br />for spatial nonstationarities existing in SAR data. The proposed<br />approach is compared to a state-of-the-art despeckling technique<br />using both simulated and real SAR data. Experimental results illustrate<br />its improvement in preserving the structural detail, especially<br />the sharpness of the edges, when suppressing speckle noise.</p>

A New Approach for Speckle Reduction in Holographic 3D printer

A Holographic 3D printer produces a high quality 3D image reproduced by a full-color, full-parallax holographic stereogram with high-density light-ray recording. But speckle-pattern noise localized behind the reconstructed image is causing a loss of the display quality. This noise is originated from the speckle generated by a diffuser for equalizing the intensity distribution of the object light on the recording medium. We analyze some conventional ways for speckle reduction using a band-limited diffuser, and it is found that these ways cannot reduce the noise sufficiently. Then we propose two methods, one introduces a moving diffuser and the other introduces multiple exposures and a digital diffuser called as 4L-PRPS.

Speckle Reduction Approach for SAR Image in Satellite Communication

Speckle Reduction Approach for SAR Image in Satellite Communication

Speckle Reduction in Ultrasound Images Using Nonisotropic Adaptive Filtering

In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.

Analysis of speckle reduction based on theory of optical eigenfunction

The principle of optical eigenfunction is used in the analysis of speckle reduction in laser projection display. When the moving diffuser is used in the speckle reduction approach, the speckle contrast is decided by both the degree of freedom (DOF) of the projector and the area of resolving cell of the eyes on the screen. It can gain high DOF of the projector by increasing the space-bandwidth product, i.e., adopting a projection lens with a high numerical aperture or a large viewing field. The best scheme is equalizing the DOF of the scattering wave from a moving diffuser to that of the projection lens. The experimental results are in accordance with the conclusion drawn by optical eigenfunction.

Speckle reduction approach for breast ultrasound image and its application to breast cancer diagnosis

Objectives To retrospectively evaluate the effects of a speckle reduction algorithm on radiologists’ diagnosis of malignant and benign breast lesions on ultrasound (US) images. Methods Using a database of 603 breast (US) images of 211 cases (109 benign lesions and 102 malignant ones), the original and speckle-reduced images were assessed by five radiologists and final assessment categories were assigned to indicate the probability of malignancy according to BI-RADS-US. The diagnostic sensitivity and specificity were investigated by the areas (Az) under the receiver operating characteristic (ROC) curves. Results The sensitivity and specificity of breast lesions on Ultrasound images improved from 88.7% to 94.3%, from 68.6% to 75.2%, respectively, and the area (Az) under ROC curve of diagnosis also increased from 0.843 to 0.939, Z = 4.969, there were significant differences in the Az between the original breast lesions and speckle-reduced ones on Ultrasound images ( P < 0.001). The diagnostic accuracy of breast lesions had been highly improved from 78.67% to 92.73% after employing this algorithm. Conclusions The results demonstrate the promising performance of the proposed speckle reduction algorithm in distinguishing malignant from benign breast lesions which will be useful for breast cancer diagnosis.

A Novel Approach to Speckle Reduction in Ultrasound Imaging

Speckle noise is inherent in ultrasound images, and it generally tends to reduce the resolution and contrast, thereby degrading the diagnostic accuracy of this modality. Speckle reduction is very important and critical for ultrasound imaging. In this paper, we propose a novel approach for speckle reduction using 2-D homogeneity and directional average filters. We have conducted experiments on numerous artificial images, clinic breast ultrasound images and vascular images. The experimental results are compared with that of other methods and the performance is evaluated using several merits, and they demonstrate that the proposed approach can reduce the speckle noise effectively without blurring the edges and damaging the textual information. It will be very useful for computer-aided diagnosis systems using ultrasound images. (E-mail: hengda.cheng@usu.edu)

Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42dB over the best state-of-the-art despeckling method reported in the literature, 1.73dB over the Lee filter and 1.31dB over the Kaun filter at an input SNR of 12.0dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.

Wavelet-based statistical approach for speckle reduction in medical ultrasound images.

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Ksigma2/sigma(x), where sigma and sigma(x) were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.

Polarimetric speckle reduction using multi-texture maximum likelihood method

A new speckle reduction approach, which estimates texture parameters simultaneously by the method of maximum likelihood under the assumption of multi-texture, is proposed. Comparison with the polarimetric whitening filter (PWF) shows that this approach preserves the polarimetric properties and reduces the couple between polarimetric channels.

Strain compounding: a new approach for speckle reduction

A new compounding technique for reducing speckle brightness variations is proposed. This method exploits the decorrelation between signals under different strain states. The different strain states can be created using externally applied forces such as the ones used in sonoelastography. Such forces produce three-dimensional tissue motion. By correcting only the in-plane (i.e., axial and lateral) motion, the images under different strain states have similar characteristics except for speckle appearance caused by the uncorrected out-of-plane (i.e., elevational) motion. Additional speckle decorrelation is also introduced through tissue motion correction caused by the change of effective in-plane sample volume geometry. Therefore, these images can be combined for speckle reduction with less degradation in in-plane spatial resolution than conventional approaches. In this paper, three-dimensional tissue motion under various strain conditions were simulated. It was found that significant speckle decorrelation existed at strains achievable in some clinical situations. Experiments were also conducted to test efficacy of this approach. Pulse-echo data from a gelatin-based phantom were acquired using a 5-MHz, single crystal transducer, and both conventional and compound B-mode images were formed. Results indicated that speckle brightness variations were reduced, and detectability of low contrast objects was enhanced. Performance limitations and fundamental differences between the proposed technique and existing techniques are discussed.

Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing.

This paper presents an algorithm for speckle reduction and contrast enhancement of echocardiographic images. Within a framework of multiscale wavelet analysis, we apply wavelet shrinkage techniques to eliminate noise while preserving the sharpness of salient features. In addition, nonlinear processing of feature energy is carried out to enhance contrast within local structures and along object boundaries. We show that the algorithm is capable of not only reducing speckle, but also enhancing features of diagnostic importance, such as myocardial walls in two-dimensional echocardiograms obtained from the parasternal short-axis view. Shrinkage of wavelet coefficients via soft thresholding within finer levels of scale is carried out on coefficients of logarithmically transformed echocardiograms. Enhancement of echocardiographic features is accomplished via nonlinear stretching followed by hard thresholding of wavelet coefficients within selected (midrange) spatial-frequency levels of analysis. We formulate the denoising and enhancement problem, introduce a class of dyadic wavelets, and describe our implementation of a dyadic wavelet transform. Our approach for speckle reduction and contrast enhancement was shown to be less affected by pseudo-Gibbs phenomena. We show experimentally that this technique produced superior results both qualitatively and quantitatively when compared to results obtained from existing denoising methods alone. A study using a database of clinical echocardiographic images suggests that such denoising and enhancement may improve the overall consistency of expert observers to manually defined borders.