Speckle Noise In Ultrasound Images Research Articles

SU-E-U-07: An Edge-Preserving Markov-Random-Fields Model for Speckle Removal in Ultrasound Images

Purpose: Contemporary ultrasound (US) systems have gained the confidence of medical community among other imaging modalities such as CT and MRI. However, US images carry a granular pattern, so called speckle, which constitutes a major image quality degradation factor. A new Markov random fields (MRF) model is proposed for the detection and removal of speckle noise in ultrasound images. Methods: 20 ultrasound images were analyzed. The MRF model design comprised two distinct sources of information: (a) the likelihood function that characterizes the contrast likelihood at a site, and (b) the a priori knowledge derived from the wavelet transform of the US image. The likelihood probability density function (pdf) is approximated by the combination of the intensity distribution and the wavelet transform modules (WTM) values of individual regions in the image. The a priori knowledge or contextual information is described by the positions and the angle vector of the WTM values. The combination of these sources builds the MRF model and provides an accurate edge map that is employed in the speckle reduction procedure that follows. Results: The proposed MRF model addresses the speckle problem that dominates US imaging. Speckle noise is reduced significantly while all edges remained intact. It exhibited similar results in terms of speckle index (SI), signal‐to‐mean‐square‐error ratio (S/mse) and edge preservation index s compared with the commercially available denoising packet introduced by General Electric termed as Speckle Reduction Imaging (SRI). Conclusion: Experimental results have demonstrated that an efficient speckle suppression algorithm can improve the overall image quality, which in turns could improve the decision‐making procedure in ultrasound imaging.

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images.

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

BackgroundUltrasonography is a convenient technique to investigate muscle properties and has been widely used to look into muscle functions since it is non-invasive and real-time. Muscle thickness, a quantification which can effectively reflect the muscle activities during muscle contraction, is an important measure for musculoskeletal studies using ultrasonography. The traditional manual operation to read muscle thickness is subjective and time-consuming, therefore a number of studies have focused on the automatic estimation of muscle fascicle orientation and muscle thickness, to which the speckle noises in ultrasound images could be the major obstacle. There have been two popular methods proposed to enhance the hyperechoic regions over the speckles in ultrasonography, namely Gabor Filtering and Multiscale Vessel Enhancement Filtering (MVEF).MethodsA study on gastrocnemius muscle is conducted to quantitatively evaluate whether and how these two methods could help the automatic estimation of the muscle thickness based on Revoting Hough Transform (RVHT). The muscle thickness results obtained from each of the two methods are compared with the results from manual measurement, respectively. Data from an aged subject with cerebral infarction is also studied.ResultsIt’s shown in the experiments that, Gabor Filtering and MVEF can both enable RVHT to generate comparable results of muscle thickness to those by manual drawing (mean ± SD, 1.45 ± 0.48 and 1.38 ± 0.56 mm respectively). However, the MVEF method requires much less computation than Gabor Filtering.ConclusionsBoth methods, as preprocessing procedure can enable RVHT the automatic estimation of muscle thickness and MVEF is believed to be a better choice for real-time applications.

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.

Reduction of Speckle Noise in Ultrasound Images Using Wavelet Techniques and its Performance Analysis

Noise can be of different types in Image processing but in medical images the noise which generally corrupts the image is speckle noise. Thus image De-noising has become very essential in the field of the biomedical image pre-processing. It is often done before the image data is to be analyzed. This paper gives a review of various different techniques for reduction of speckle noise in ultrasound images. Ultrasonic imaging is a widely used medical imaging procedure because it is comparatively safe, economical, adaptable, and transferable. Though, one of its main disadvantages is the poor quality of images, which are corrupted by speckle noise. The presence of speckle is unattractive since it degrades image quality and it affects the tasks of individual interpretation and diagnosis. This paper demonstrates wavelet based techniques for improving visual image quality in ultrasound images and de-noising. With the help of variable window technique, region based processing and discrete wavelet transform technique provides better noise rejection in ultrasound images by removing the speckle noise.

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

The purpose of this paper is to present a methodology to estimate the carotid artery lumen centerlines in ultrasound (US) images obtained in a free-hand examination. Challenging aspects here are speckle noise in US images, artifacts, and the lack of contrast in the direction orthogonal to the US beam direction. An algorithm based on a rough lumen segmentation obtained by robust ellipse fitting was developed to deal with these conditions and estimate the lumen center in 2D B-mode scans. In a free-hand sweep examination, continuous image acquisitions are performed through time when the radiologist moves the probe on the patient's neck. The result is a series of images that show 2D cross-sections of the carotid's morphology. A tracking sensor (Flock of Birds) was attached to the probe and both were connected to a PC executing the Stradwin software, which relates spatial information to the acquisition data of the US probe. The spatial information was combined with the 2D lumen center estimates to provide a centerline in 3D. For validation, 19 carotid scans from 15 different patients were scanned, their centerlines calculated by the algorithm and compared with results acquired by manual annotations. The average Euclidean distance between both among all the examinations was 0.82 mm. For each examination, the percentage of these Euclidean distances below 2 mm was calculated; the average over all examinations was 92%. Automated 3D estimation of carotid artery lumen centerlines in free-hand real-time ultrasound is feasible and can be performed with high accuracy. The algorithm is robust enough to keep the centerlines inside the vessel, even in the absence of contrast in parts of the vessel wall.

A Variational Model to Remove the Multiplicative Noise in Ultrasound Images

In this paper we study a variational model to deal with the speckle noise in ultrasound images. We prove the existence and uniqueness of the minimizer for the variational problem, and derive the existence and uniqueness of weak solutions for the associated evolution equation. Furthermore, we show that the solution of the evolution equation converges weakly in BV and strongly in L 2 to the minimizer as t??. Finally, some numerical results illustrate the effectiveness of the proposed model for multiplicative noise removal.

Medical ultrasound image denoising based on improved anisotropic diffusion equation

An improved method based on anisotropic diffusion equation was proposed. By expanding the Perona and Malik anisotropic diffusion model (P-M model) to include diagonal edges,more image details were added. And a new approach of calculating the diffusion coefficient overcame the high rate of convergence;the novel gradient operator could distinguish noise points and detect edge. The simulation results show that the performance of the method is superior to the classical P-M equation and Linshi operator in image smoothing and edge preserving,as well as the reduction of iteration time. The method is an effective way of suppressing speckle noise in ultrasound images.

Comparative Study on Speckle Noise Suppression Techniques for Ultrasound Images

In medical image processing, image denoising has become a very essential exercise all through the diagnose. Negotiation between the preservation of useful diagnostic information and noise suppression must be treasured in medical images. In certain cases, for instance in Ultrasound images, the noise can restrain information which is valuable for the general practitioner. The success of ultrasonic examination depends on the Image quality. In case of ultrasonic images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. Many denoising techniques have been proposed for effective suppression of speckle noise. This paper presents the performance analysis of various schemes for suppressing speckle noise in Ultrasound images in terms of the assessment parameters PSNR and Equivalent Number of Looks (ENL).

Finding out general tendencies in speckle noise reduction in ultrasound images

This article investigates and compiles some of the techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. With this information, a comparison of all the methods studied is done based on an experiment, using quality metrics to test their performance and show the benefits each one can contribute. To test the methods, a synthetic, noise-free image of a kidney is created and later simulations using Field II program to corrupt it are performed. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. Since real ultrasound images are already noise corrupted images and real noise-free images do not exist, conventional metrics cannot be used to indicate the quality obtained with filtering. Nevertheless, we propose the use of the tendencies observed in our study in real images.

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.

The maltese cross processor: Speckle reduction for circular transducers

A new online signal processing technique is described to reduce speckle noise in ultrasound images. In the imaging system, a focused piston transducer is divided into thirty-two sectors. In the receive mode, parallel signal processing arranges the sectors into eight maltese crosses. The rf signals of the perpendicular arms of each cross are multiplied in a phase sensitive process. The orthogonal receive mode multiplication is designed to reduce side lobes resulting from the sector shapes while maintaining lateral resolution through the use of the full aperture diameter. The signals from the crosses are then combined via postdetection summation. Six of the eight crosses perform successfully. The six maltese crosses show decorrelated signals equivalent to four independent samples of the speckle noise which decreases noise contrast by a factor of two with no measureable loss of spatial resolution. Post summation compression is included to retain the conventional signal dynamic range. Parallel signal processing maintains the normal image line rate. Images of tissue-mimicking phantoms including speckle targets show improved detectability of simulated lesions.

The Maltese cross processor: speckle reduction for circular transducers.

A new online signal processing technique is described to reduce speckle noise in ultrasound images. In the imaging system, a focused piston transducer is divided into thirty-two sectors. In the receive mode, parallel signal processing arranges the sectors into eight maltese crosses. The rf signals of the perpendicular arms of each cross are multiplied in a phase sensitive process. The orthogonal receive mode multiplication is designed to reduce side lobes resulting from the sector shapes while maintaining lateral resolution through the use of the full aperture diameter. The signals from the crosses are then combined via postdetection summation. Six of the eight crosses perform successfully. The six maltese crosses show decorrelated signals equivalent to four independent samples of the speckle noise which decreases noise contrast by a factor of two with no measureable loss of spatial resolution. Post summation compression is included to retain the conventional signal dynamic range. Parallel signal processing maintains the normal image line rate. Images of tissue-mimicking phantoms including speckle targets show improved detectability of simulated lesions.