Speckle Reducing Anisotropic Diffusion Research Articles

Complex wavelet diffusion for enhancing ultrasound images of the laminar bone

The aim of this paper is to remove speckle noise and enhance the laminar bone structure in ultrasound images in order to aid the anesthesiologist during an epidural anesthesia needle insertion. In this regard, a direction and scale sensitive anisotropic diffusion (AD) algorithm called the complex wavelet diffusion algorithm is proposed. The proposed algorithm is tested on synthetic and real ultrasound images of the laminar bone and the results are compared with the speckle-reducing AD method. Results indicate that the proposed method can better highlight the diagonal-shaped laminar bones while removing speckle noise. Furthermore, the proposed despeckling algorithm is very fast providing the anesthesiologist with real-time information while an epidural injection is performed.

Techniques to derive geometries for image-based Eulerian computations.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

Application of special-purpose artificial neural networks for speckle reduction in SAR images

Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures.

基于同质区域自动选取的各向异性扩散超声图像去噪

An adaptive selection method of diffusion threshold was proposed to improve the effectiveness and stability of a filter in speckle reduction of ultrasound images.An optimal threshold of the ultrasound image was determined by the Otsu binarization algorithm.Then,the ultrasound image was divided into blocks by Quad tree decomposition using the optimal threshold as the criterion of homogeneity.In descending order of the size,the present maximal blocks were picked up from the Quad tree decomposition result,and an optimal homogeneous region of the ultrasound image was selected by theproposed selection criteria.Finally,the diffusion threshold was obtained by analyzing statistical features of the optimal homogeneous region,and the ultrasound image was filtered using this diffusion threshold.The results demonstrate that the proposed method has better performance comparing with the Speckle Reducing Anisotropic Diffusion(SRAD)method and the Detail Preserving Anisotropic Diffusion(DPAD)method.It reduces the operation time effectively,and the average figure-of-merit by using the proposed method is 0.029,0.129higher than those by using other two mentioned methods. The proposed method avoids the manual selection of homogeneous area and can estimate the diffusion threshold accurately,which can reduce the speckles effectively while preserving the edges.

Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering

Multiplicative speckle noise which is inherently present in medical ultrasound images degrades the important clinical informations and badly affects the quality of the diagnosis. It is necessary to reduce the speckle noise to improve the visual quality of ultrasound images for better diagnoses. In this paper, a wavelet based method for despeckling of the ultrasound images is introduced where a local Wiener filter along with speckle reducing anisotropic diffusion (SRAD) filter are employed in a homomorphic framework. The signal variance in the local wiener filter is estimated from the output image of the SRAD filter. Since the size and shape of the locally adaptive window is an important issue in estimating the signal variance, nearly arbitrarily shaped windows are used for better performance. The experimental results using synthetically speckled ultrasound images show that the speckle noise is reduced to a great extent while preserving the important clinical information. In order to demonstrate the effectiveness of the proposed method, the method is compared with several other existing methods in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), edge preservation index ( ), and standard deviation to mean (S/M) ratio.

Speckle reduction by image entropy anisotropic diffusion

Edge detection is the focus and difficult point for speckle reduction anisotropic diffusion. Traditional methods typically rely on the estimation of mean and variance in edge detection. The precision of the estimations influences the accuracy of edge detection. In this paper, we present an anisotropic diffusion filter based on image entropy. This method uses image entropy as an edge detector to eliminate the error due to the estimation of mean and variance, and improves the edge detection capability. The experimental results show that the visual quality and evaluation indexes of the new method outperform those of the traditional methods.

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.

Speckle Reduction for Ultrasonic Imaging Using Frequency Compounding and Despeckling Filters along with Coded Excitation and Pulse Compression

A method for improving the contrast-to-noise ratio (CNR) while maintaining the −6 dB axial resolution of ultrasonic B-mode images is proposed. The technique proposed is known as eREC-FC, which enhances a recently developed REC-FC technique. REC-FC is a combination of the coded excitation technique known as resolution enhancement compression (REC) and the speckle-reduction technique frequency compounding (FC). In REC-FC, image CNR is improved but at the expense of a reduction in axial resolution. However, by compounding various REC-FC images made from various subband widths, the tradeoff between axial resolution and CNR enhancement can be extended. Further improvements in CNR can be obtained by applying postprocessing despeckling filters to the eREC-FC B-mode images. The despeckling filters evaluated were the following: median, Lee, homogeneous mask area, geometric, and speckle-reducing anisotropic diffusion (SRAD). Simulations and experimental measurements were conducted with a single-element transducer (f/2.66) having a center frequency of 2.25 MHz and a −3 dB bandwidth of 50%. In simulations and experiments, the eREC-FC technique resulted in the same axial resolution that would be typically observed with conventional excitation with a pulse. Moreover, increases in CNR of 348% were obtained in experiments when comparing eREC-FC with a Lee filter to conventional pulsing methods.

Speckle Noise Reduction in Ultrasound Biomedical B-Scan Images Using Discrete Topological Derivative

Over three decades, several despeckling techniques have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. The topological derivative (TD) is the recently adopted technique in the area of biomedical image processing. In this work, we computed the topological derivative for an appropriate function associated to the ultrasound B-scan image gradient by assigning a diffusion factor k, which indicates the cost endowed to that particular image. In this article, a novel image denoising approach, called discrete topological derivative (DTD) has been implemented. The algorithm has been developed in MATLAB7.1 and tested over 200 ultrasound B-scan images of several organs such as the liver, kidney, gall bladder and pancreas. Further, the performance of the DTD algorithm has been estimated by calculating important performance metrics. A comparative study was carried out between the DTD and the traditional despeckling techniques. The calculated peak signal-to-noise ratio (PSNR) (the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation) value of the DTD despeckled liver image is found to be 28 which is comparable with the outperformed speckle reducing anisotropic diffusion (SRAD) filter. SRAD filter is an edge-sensitive diffusion method for speckled images of ultrasonic and radar imaging applications. Canny edge detection and visual inspection of DTD filtered images by the trained radiologist found that the DTD algorithm preserves the hypoechoic and hyperechoic regions resulting in improved diagnosis as well as tissue characterization.

A non-linear complex diffusion based filter adapted to Rayleigh's speckle noise for de-speckling ultrasound images

In this paper, an edge and structure preserving non-linear complex diffusion based filter adapted to Rayleigh’s speckle noise for speckle reduction from 2D ultrasound images is proposed and implemented in MATLAB 7.0. The initial condition of the proposed filter is the speckle noised ultrasound image and the speckle-reduced image is obtained after certain iterations of the filter till its convergence. For digital implementations, the proposed scheme has been discretised using the finite difference scheme. The performance of the proposed complex diffusion-based filter has been evaluated both qualitatively and quantitatively. A comparative study of the proposed scheme with other standard speckle reduction schemes such as the homomorphic Wiener filter, the Lee filter, the Frost filter, the Kuan filter and the Speckle Reducing Anisotropic Diffusion (SRAD) filter for several ultrasound images with varying amounts of speckle noise variance is also presented. The obtained results show that the proposed non-linear complex diffusion-based scheme performs better than all other schemes in consideration and is also well capable of preserving edges and fine structures from ultrasound images during speckle reduction.

A comparative analysis of diffusion-based and wavelet-based approaches for despeckling of ultrasound medical images

In this paper, Speckle Reduction Anisotropic Diffusion (SRAD) and the modified SRAD approach from spatial domain theme and Wavelet-Transform-based Thresholding Neural Network (WT-TNN) approach from transform domain theme have been extensively analysed based on the speckle suppression and edge preservation of ultrasound medical images and standard images. The results of such comparative analysis of two themes indicate that predicting the number of iteration required for optimum performance is a major concern for SRAD and MSRAD, whereas in case of WT-TNN approach, more despeckling time is a major limitation although it gives better speckle suppression than SRAD and MSRAD.

An adaptive non-linear PDE-based speckle reduction technique for ultrasound images

In this paper, an adaptive nonlinear complex diffusion based filter is proposed to reduce the speckle noise from ultrasound images. The adaptive and modified version of a fourth order PDE is also proposed and examined for its suitability and efficacy for speckle reduction from ultrasound images. The performance of the proposed method is compared with constraint driven anisotropic diffusion based method, modified fourth order PDE based method and other methods such as Speckle Reducing Anisotropic Diffusion (SRAD) filter, Lee filter, Frost filter and Kuan filter in terms of various performance metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Correlation Parameter (CP) and Mean Structure Similarity Index Map (MSSIM). The obtained results justify the applicability of the proposed scheme.

A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.

Enhancement of the ultrasound images by modified anisotropic diffusion method

Speckle is a primary factor which degrades the contrast resolution and masks the meaningful texture information present in an ultrasound image. Its presence severely hampers the interpretation and analysis of ultrasound images. When speckle reduction technique is applied for visual enhancement of ultrasound images, it is to be kept in mind that blurring associated with speckle reduction should be less and fine details are properly enhanced. With these points in consideration, the modified speckle reduction anisotropic diffusion (MSRAD) method is proposed in the present study to improve the visual quality of the ultrasound images. In the proposed MSRAD method, the four neighboring pixel template in speckle reduction anisotropic diffusion (SRAD) method of Yu and Acton (IEEE Trans Image Process 11:1260-1270, 2002) have been replaced by a new template of larger number of neighboring pixels to calculate the diffusion term. To enhance visual quality of ultrasound images, nonquadratic regularization (Yu and Yadegar, Proceedings of the IEEE international conference on image processing, 2006) is incorporated with MSRAD method and accordingly changes in parameter settings have been made. The performance of MSRAD method was evaluated using clinical ultrasound images, interpretation by the medical experts and results of MSRAD method by subjective and objective criteria.

Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images

In this paper, the partial differential equation (PDE) based homomorphic filtering technique is proposed for speckle reduction from digitally reconstructed holographic images based on the concepts of complex diffusion processes. For digital implementations, the proposed scheme was discretized using finite differences scheme. Further, the performance of the proposed PDE-based technique is compared with other speckle reduction techniques such as homomorphic anisotropic diffusion filter based on extended concept of Perona and Malik (1990) [2], homomorphic Weiner filter, Lee filter, Frost filter, Kuan filter, speckle reducing anisotropic diffusion (SRAD) filter and hybrid filter in the context of digital holography. For the comparison of various speckle reduction techniques, the performance is evaluated quantitatively in terms of all possible parameters that justify the applicability of a scheme for a specific application. The chosen parameters are mean-square-error (MSE), normalized mean-square-error (NMSE), peak signal-to-noise ratio (PSNR), speckle index, average signal-to-noise ratio (SNR), effective number of looks (ENL), correlation parameter (CP), mean structure similarity index map (MSSIM) and execution time in seconds. For experimentation and computer simulation MATLAB 7.0 has been used and the performance is evaluated and tested for various sample holographic images for varying amount of speckle variance. The results obtained justify the applicability of proposed schemes.

Enhancing obstetric and gynecology ultrasound images by adaptation of the speckle reducing anisotropic diffusion filter

So far there is no ideal speckle reduction filtering technique that is capable of enhancing and reducing the level of noise in medical ultrasound (US) images, while efficiently responding to medical experts' validation criteria which quite often include a subjective component. This paper presents an interactive tool called evolutionary speckle reducing anisotropic diffusion filter (EVOSRAD) that performs adaptive speckle filtering on ultrasound B-mode still images. The medical expert runs the algorithm interactively, having a permanent control over the output, and guiding the filtering process towards obtaining enhanced images that agree to his/her subjective quality criteria. We employ an interactive evolutionary algorithm (IGA) to adapt on-line the parameters of a speckle reducing anisotropic diffusion (SRAD) filter. For a given input US image, the algorithm evolves the parameters of the SRAD filter according to subjective criteria of the medical expert who runs the interactive algorithm. The method and its validation are applied to a test bed comprising both real and simulated obstetrics and gynecology (OB/GYN) ultrasound images. The potential of the method is analyzed in comparison to other speckle reduction filters: the original SRAD filter, the anisotropic diffusion, offset and median filters. Results obtained show the good potential of the method on several classes of OB/GYN ultrasound images, as well as on a synthetic image simulating a real fetal US image. Quality criteria for the evaluation and validation of the method include subjective scoring given by the medical expert who runs the interactive method, as well as objective global and local quality criteria. The method presented allows the medical expert to design its own filters according to the degree of medical expertise as well as to particular and often subjective assessment criteria. A filter is designed for a given class of ultrasound images and for a given medical expert who will later use the respective filter in clinical practice. The process of designing a filter is simple and employs an interactive visualization and scoring stage that does not require image processing knowledge. Results show that filters tailored using the presented method achieve better quality scores than other more generic speckle filtering techniques.

Nonlinear Diffusion in Laplacian Pyramid Domain for Ultrasonic Speckle Reduction

A new speckle reduction method, i.e., Laplacian pyramid-based nonlinear diffusion (LPND), is proposed for medical ultrasound imaging. With this method, speckle is removed by nonlinear diffusion filtering of bandpass ultrasound images in Laplacian pyramid domain. For nonlinear diffusion in each pyramid layer, a gradient threshold is automatically determined by a variation of median absolute deviation (MAD) estimator. The performance of the proposed LPND method has been compared with that of other speckle reduction methods, including the recently proposed speckle reducing anisotropic diffusion (SRAD) and nonlinear coherent diffusion (NCD). In simulation and phantom studies, an average gain of 1.55 dB and 1.34 dB in contrast-to-noise ratio was obtained compared to SRAD and NCD, respectively. The visual comparison of despeckled in vivo ultrasound images from liver and carotid artery shows that the proposed LPND method could effectively preserve edges and detailed structures while thoroughly suppressing speckle. These preliminary results indicate that the proposed speckle reduction method could improve image quality and the visibility of small structures and fine details in medical ultrasound imaging.

Segmentation of the prostate from suprapubic ultrasound images

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.

Speckle reducing anisotropic diffusion for 3D ultrasound images

This paper presents an approach for reducing speckle in three dimensional (3D) ultrasound images. A 2D speckle reduction technique, speckle reducing anisotropic diffusion (SRAD), is explored and extended to 3D. 3D SRAD is advantageous in that, like 2D SRAD, it keeps the advantages of the conventional anisotropic diffusion and the traditional speckle reducing filter, the Lee filter, by exploiting the instantaneous coefficient of variation (ICOV). Besides, 3D SRAD uses 3D information; thus it overcomes the shortcoming of the 2D technique that only uses 2D information. The algorithm of 3D SRAD is presented in the continuous domain as well as in the discrete domain. Experiments have been performed on both synthetic and real 3D ultrasound images and the experimental results were compared with those obtained by 3D anisotropic diffusion and the 3D Lee filter. The experimental results show that the quality of the 3D SRAD for speckle reduction in 3D ultrasound images improves upon that of 3D anisotropic diffusion and 3D Lee filter in terms of edge preservation and the smoothness of homogenous regions.

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.