Real Ultrasound Images Research Articles

Development of an ultrasound image processing approach to enable automated needle localisation for epidural anesthesia

In this paper, ultrasound imaging is utilised to detect the anatomical structure of lumbar spine, in order to automatically search for a proper epidural needle insertion site. Induced by random speckle noise, ultrasound images are generally obscure and have low resolution, which decrease the readability of the image content. In order to improve the interpretability of ultrasound images, local normalisation modified with difference of Gaussian (DoG) algorithm is used for pre-processing. The pre-processing procedure successfully filters the noise and extracts the main anatomical structures; meanwhile it removes the local means induced by non-uniform wave reflection rate. Then, template matching algorithm, augmented with a position correlator, is utilised to automatically identify the features of interest. Based on the result of feature matching, proper needle insertion site will be identified automatically. The approach has been tested on more than 200 ultrasound images with a 100% success rate. The proposed system allows the anesthetist to use the approach efficiently without the burden of interpreting real time ultrasound images.

Active Contour Model for Ultrasound Images with Rayleigh Distribution

Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method.

A New Contourlet-based Compression and Speckle Reduction Method for Medical Ultrasound Images

In this paper a threshold based method for speckle reduction and image compression of medical ultrasound images was presented. First, two ultrasound medical image despeckling methods were compared: wavelet-based and contourlet-besd, to find the best. Different measures were used for performance comparison and these methods were implemented both on synthesized data and real ultrasound images. It is found that, performance of the both techniques vary with the level of the speckle noise, and in the case of preserving image details and edges which is very important for medical image processing, contourlet-based method shows better performance over wavelet-based speckle reduction specially in high levels of noise. Then, a new contourlet-based lossy image compression method was presented for medical ultrasound images. In this algorithm, contourlet transform was used for image decomposition. Then, a new thresholding process was applied on the coefficients before quantization. The compression threshold was elected due to coefficients occurrence in the contourlet domain. This algorithm has the ability of simultaneous speckle reduction using another thresholding. Due to this time saving ability, the algorithm can be used in online image transmission systems. The proposed method was implemented on a real ultrasound images and ultrasound phantom image. Results proved that our proposed method has acceptable and good performance over common compression methods such as wavelet-based SPIHT in the case of PSNR.

Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer

Segmentation of ultrasound (US) images of breast cancer is one of the most challenging problems of the modern medical image processing. A number of popular codes for US segmentation are based on a generalized gradient vector flow (GGVF) method proposed by Xu and Prince. The GGVF equations include a smoothing term (diffusion) applied to regions of small gradients of the edge map and a stopping term to fix and extend large gradients appearing at the boundary of the object.The paper proposes two new directions. The first component is diffusion as a polynomial function of the intensity of the edge map. The second component is the orientation score of the vector field. The new features are integrated into the GGVF equations in the smoothing and the stopping term.The algorithms, having been tested by a set of ground truth images, show that the proposed techniques lead to a better convergence and better segmentation accuracy with the reference to conventional GGVF snakes. The adaptive multi-feature snake does not require any hand-tuning. However, it is as efficient as the standard GGVF with the parameters selected by the “brutal force approach”. Finally, proposed approach has been tested against recent modifications of GGVF, i.e. the Poisson gradient vector flow, the mixed noise vector flow and the convolution vector flow. The numerical tests employing 195 synthetic and 48 real ultrasound images show a tangible improvement in the accuracy of segmentation.

Ultrasound kidney segmentation with a global prior shape

In this paper, we focus on segmentation of ultrasound kidney images. Unlike previous work by using trained prior shapes, we employ a parametric super-ellipse as a global prior shape for a human kidney. The Fisher–Tippett distribution is employed to describe the grey level statistics. Combining the grey level statistics with a global character of a kidney shape, we propose a new active contour model to segment ultrasound kidney images. The proposed model involves two subproblems. One subproblem is to optimize the parameters of a super-ellipse. Another subproblem is to segment an ultrasound kidney image. An alternating minimization scheme is used to optimize the parameters of a super-ellipse and segment an image simultaneously. To segment an image fast, a convex relaxation method is introduced and the split Bregman method is incorporated to propose a fast segmentation algorithm. The efficiency of the proposed method is illustrated by numerical experiments on both simulated images and real ultrasound kidney images.

Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound

Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm-support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6% with sensitivity of 90% and 93.3% for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies.

Quantitative Comparison of Speckle Smoothing for Ultrasound Images Using Besov Norm

This paper presents a novel speckle smoothing method for ultrasound images. This smoothing method is designed to preserve both the edges and structural details of the image. Speckle noise is suppressed by extending the smoothness of the image in the wavelet-based Hölder spaces. We try to solve the performance of speckle reduction problem from the viewpoint of Besov norm. A comparison of smoothing speckles with the other well-known methods is provided via the size of Besov norm. We validate the proposed method using synthetic data, simulated and real ultrasound images. Experiments demonstrate the performance improvement of the proposed method over other state-of-the-art methods in terms of image quality and edge preservation indices. Keywords: Function spaces, speckle reduction, ultrasound image, wavelet transforms, Assessment Parameters, Gaussian noise, multi-resolution analysis, Structural SIMilarity, despeckling methods, FOM

Real-Time GPU-Based Ultrasound Simulation Using Deformable Mesh Models

This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.

Nonlinear Diffusion based on Bayesian Estimator in Laplacian Pyramid Domain for Ultrasonic Speckle Reduction

Laplacian pyramid based nonlinear diffusion (LPND) method is proposed for speckle noise reduction in medical ultrasound imaging. In 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 Bayesian threshold is automatically determined by a variation of robust median estimator. The proposed LPND method reduces speckle noise significantly while preserving sharp image details. The superiority of the proposed LPND over the conventional LPND based on Gaussian filtering and LPND method based on Butterworth filtering based speckle reduction technique is demonstrated by applying them on synthetic and real ultrasound B-mode images.

A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE.

BackgroundComputer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL) and Radio-Frequency Ablation (RFA) of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention.MethodsIn this paper, we proposed a novel framework for kidney segmentation of ultrasound (US) images combined with nonlocal total variation (NLTV) image denoising, distance regularized level set evolution (DRLSE) and shape prior. Firstly, a denoised US image was obtained by NLTV image denoising. Secondly, DRLSE was applied in the kidney segmentation to get binary image. In this case, black and white region represented the kidney and the background respectively. The last stage is that the shape prior was applied to get a shape with the smooth boundary from the kidney shape space, which was used to optimize the segmentation result of the second step. The alignment model was used occasionally to enlarge the shape space in order to increase segmentation accuracy. Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm.ResultsWe applied our segmentation framework on synthetic and real US images to demonstrate the better segmentation results of our method. From the qualitative results, the experiment results show that the segmentation results are much closer to the manual segmentations. The sensitivity (SN), specificity (SP) and positive predictive value (PPV) of our segmentation result can reach 95%, 96% and 91% respectively; As well as we compared our results with the edge-based level set and level set with shape prior method by means of the same quantitative index, such as SN, SP, PPV, which have corresponding values of 97%, 88%, 78% and 81%, 91%, 80% respectively.ConclusionsWe have found NLTV denosing method is a good initial process for the ultrasound segmentation. This initial process can make us use simple segmentation method to get satisfied results. Furthermore, we can get the final segmentation results with smooth boundary by using the shape prior after the segmentation process. Every step enjoy simple energy model and every step in this framework is needed to keep a good robust and convergence property.

Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain

Speckle reduction is a difficult task for ultrasound image processing because of low resolution and contrast. As a novel tool of image analysis, quaternion wavelet (QW) has some superior properties compared to discrete wavelets, such as nearly shift-invariant wavelet coefficients and phase-based texture presentation. We aim to exploit the excellent performance of speckle reduction in quaternion wavelet domain based on the soft thresholding method. First, we exploit the characteristics of magnitude and phases in quaternion wavelet transform (QWT) to the denoising application, and find that the QWT phases of the images are little influenced by the noises. Then we model the QWT magnitude using the Rayleigh distribution, and derive the thresholding criterion. Furthermore, we conduct several experiments on synthetic speckle images and real ultrasound images. The performance of the proposed speckle reduction algorithm, using QWT with soft thresholding, demonstrates superiority to those using discrete wavelet transform and classical algorithms.

SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors

A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6%. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8% with sensitivity of 90% for detecting normal and cirrhotic cases and sensitivity of 86.6% for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6% for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.

Speckle reduction with edges preservation for ultrasound images: using function spaces approach

In this study, a novel speckle reduction method is proposed for ultrasound images. This denoising method is designed to preserve both the edges and structural details of the image. Speckle noise is suppressed, without smearing the edges, by extending the smoothness of the image in the wavelet-based Hölder spaces. A comparison of smoothing speckles with the other well-known methods is provided via the size of Besov norm. The authors validate the proposed method using synthetic data, simulated and real ultrasound images. Experiments demonstrate the performance improvement of the proposed method over other state-of-the-art methods in terms of image quality and edge preservation indices.

Fast reduction of speckle noise in real ultrasound images

In this paper, we concentrate on fast removing speckle noise in real ultrasound images. It is hard to design a fast algorithm to solve a speckle reduction model, since the data fitting term in a speckle reduction model is usually not convex. In this paper, we present a convex variational model to deal with speckle noise in real ultrasound images. The data-fitting term of the proposed model is obtained by using a generalized Kullback–Leibler distance. To fast solve the proposed model, we incorporate variable splitting method and Bregman iterative method to propose a fast ultrasound speckle reduction algorithm. The capability of the proposed method is shown both on synthetic images and real ultrasound images.

Cryo-preserved porcine kidneys are feasible for teaching and training renal biopsy: “the bento kidney”

BackgroundThe use of patients as the primary teaching modality for learning procedures is being questioned. While there have been advancements in the technology used for performing needle biopsies in both native and transplanted kidneys, there has been little advancement in teaching and training tools. We have developed a portable ex-vivo kidney, the Bento Kidney, using cryo-preserved porcine kidneys for teaching this procedure.MethodsThe kidney is thawed, perfused by a pump, covered with skin for realistic haptic feedback, and then used with existing biopsy technology to teach the technique.ResultsThirty porcine kidneys were used in this pilot research, and nine were shipped to physicians at a distant facility. Renal biopsy was then performed using a core biopsy needle and ultrasound guidance. There was some leakage of fluid from all kidneys noted. All trainees felt that the model was realistic, and judged at a mean score of 8.7 (SD 0.8) on a scale of 1 (not useful) to 10 (very useful).ConclusionsThis feasibility study demonstrates that cryo-preserved porcine kidneys can be successfully used to teach and train renal biopsy techniques, and provides haptic feedback as well as realistic real-time ultrasound images. Further large scale studies are needed to demonstrate value from the educational point of view for nephrology and transplantation.

Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion

Speckle is a form of multiplicative and locally correlated noise which degrades the signal-to-noise ratio (SNR) and contrast resolution of ultrasound images. This paper presents a new anisotropic level set method for despeckling low SNR, low contrast ultrasound images. The coefficient of variation, a speckle-robust edge detector is embedded in the well known geodesic "snakes" model to smooth the image level sets, while preserving and sharpening edges of a speckled image. The method achieves much better speckle suppression and edge preservation compared to the traditional anisotropic diffusion based despeckling filters. In addition, the performance of the filter is less sensitive to the speckle scale of the image and edge contrast parameter, which makes it more suitable for the detection of low contrast features in an ultrasound image. We validate the method using both synthetic and real ultrasound images and quantify the performance improvement over other state-of-the-art algorithms in terms of speckle noise reduction and edge preservation indices.

Comparison of Block Matching and Differential Methods for Motion Analysis of the Carotid Artery Wall From Ultrasound Images

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF (HS)), weighted least-squares optical flow (OF (LK(WLS))), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF (LK(WLS)) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF (HS) (11.57 pixels). Due to its superior performance, OF (LK(WLS)) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF (LK(WLS)) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.

Medical Image Denoising by Improved Kuan Filter

This paper focuses on the issue of speckle noise and its suppression. Firstly, the multiplicative speckle noise model and its mathematical formulation are introduced. Then, certain de-noising methods are described together with possible improvements. On their basis, an improvement of Kuan method (KuanS) is proposed. Performance of proposed KuanS method is tested on real ultrasound images and synthetic images corrupted with speckle noise. PSNR, edge preservation, standard deviation of homogenous regions and SIR are used for the evaluation of quality of noise suppression. Performance of the KuanS is compared with other methods. The KuanS method achieves satisfactory results even in comparison with more complex methods (SRAD, wavelet based noise suppression).

Simulation Model for Assessing Quality of Ultrasound Strain Estimation in Abdominal Aortic Aneurysm

The purpose of this study was to develop a simulation model for evaluating methods for ultrasound strain estimation in abdominal aortic aneurysms. Wall geometry was obtained from a real ultrasound image and wall motion was simulated applying realistic blood pressures to a nonlinear viscoelastic wall model. The ultrasound simulation included speckle, absorption and angle dependent reflection. Gaussian white noise was added to simulate various noise levels. Despite not fully replicating real ultrasound images, the model simulated realistic circumferential variations in intensity and realistic speckle patterns and has potential for initial evaluation of strain estimation methods.

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.