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Proceedings ArticleDOI

Efficient Speckle Reduction Techniques in Ultrasound Video Quality Enhancement – A Comparative Evaluation

01 Sep 2017-
TL;DR: In the present work seven different filters are listed and experimental work is carried out on LEE and Hybrid Median filter (HMF), the experimental result of PSNR and MSE values are considered for evaluation purpose.
Abstract: Amongst many imaging modalities available now a days, Ultrasound is the most widely used because of the reasons like radiation free, real time, non-invasive and non-ionizing During patient’s care, medical images, videos & other physiological signals, become an integral part of diagnostic and treatment phases Ultrasound is more advantageous and cheaper than other modalities like MRI, CT, PET Speckle noise present in US hampers the quality of images which in turn considerably increases the difficulty in medical visual inspection The diagnostic accuracy will be more if image is less noisy, this necessitates the use of efficient despeckling filter which will take care of edge detection, loss of information and improve visual evaluation MSE assess the image quality, if the value is high then image quality is poor PSNR is another quality metrics, high value indicates the better denoising algorithm In the present work seven different filters are listed and experimental work is carried out on LEE and Hybrid Median filter (HMF) The experimental result of PSNR and MSE values are considered for evaluation purpose
Topics: Speckle noise (58%), Image quality (56%), Median filter (54%), Edge detection (50%)
Citations
More filters

Journal ArticleDOI
01 Feb 2021-
TL;DR: A comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity.
Abstract: Rheumatic heart disease has a substantial impact on morbidity and mortality for both men and women in developing countries. It is a complication of autoimmune phenomenon known as acute rheumatic fever in response to group A streptococcus bacteria. It often causes damage to valves and lacks its functionality. Initial manifestations of prolapsed valves are evident in echocardiography in the form of valve bulging, commissural fusion and restricted leaflet motion. However, these echocardiogram images are inevitably degraded by multiplicative noise known as speckle noise at the time of image acquisition and transmission. Hence despeckling is of vital importance for enhancing ultrasound image quality. In this paper, a comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement has been performed. In addition to this, the performance of these filters is validated using different quantitative metrics. The experimental result shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity. In conclusion, the preprocessing intends to suppress speckle noise and enhancing perceived visual quality that aids for further development of virtual cardiac model.

Journal ArticleDOI
TL;DR: Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work and an algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).
Abstract: In the medical field various motion tracking techniques like block matching, optical flow, and histogram of oriented optical flow (HOOF) are being experimented for the abnormality detection. The information furnished by the existing techniques is inadequate for medical diagnosis. This technique has an inherent drawback, as the entire image is considered for motion vector calculation, increasing the time complexity. Also, the motion vectors of unwanted objects are getting accounted during abnormality detection, leading to misidentification / misdiagnosis. In this research, our main objective is to focus more on the region of abnormality by avoiding the unwanted motion vectors from the rest of the portion of the heart, allowing better time complexity. Proposed a region-based HOOF (RHOOF) for blood motion tracking and estimation; after experimentation, it is observed that RHOOF is four times faster than HOOF. The performance of supervised machine learning techniques was evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve. In the medical field more importance is given to the sensitivity than accuracy. Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work. An algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).

References
More filters

Journal ArticleDOI
TL;DR: Experimental results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
Abstract: Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.

2,513 citations


"Efficient Speckle Reduction Techniq..." refers methods in this paper

  • ...Lee Filter: Works on multiplicative model, local statistics is used to effectively preserve edges, smoothing is performed if variance is high or not constant [30],[35]....

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Book
01 Jan 1998-
TL;DR: This work states that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition, which means that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution.
Abstract: Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

2,425 citations


"Efficient Speckle Reduction Techniq..." refers background in this paper

  • ...This distribution is done with high to low concentration areas [36]....

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Journal ArticleDOI
TL;DR: A model for the radar imaging process is derived and a method for smoothing noisy radar images is presented and it is shown that the filter can be easily implemented in the spatial domain and is computationally efficient.
Abstract: Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

1,772 citations


"Efficient Speckle Reduction Techniq..." refers methods in this paper

  • ...Frost filter: An adaptive digital filter for multiplicative noise was proposed by Frost that provides minimum MSE inside homogeneous areas of an image while preserving the edge structure, given by following equation [38],[30]....

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Journal ArticleDOI
TL;DR: This work model the speckle according to the exact physical process of coherent image formation and accurately represents the higher order statistical properties of speckel that are important to the restoration procedure.
Abstract: Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.

661 citations


Proceedings ArticleDOI
17 Mar 1983-
TL;DR: This work model the speckle according to the exact physical process of coherent image formation and accurately represents the higher order statistical properties of speckel that are important to the restoration procedure.
Abstract: Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

249 citations


"Efficient Speckle Reduction Techniq..." refers background or methods in this paper

  • ...Performance of the filter considers equivalent number of looks (ENL) calculated from an US image for evaluation of weighting function Wf, given in the following equation [37],[30]....

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  • ...Enhanced Frost filter: Enhanced version of Frost filter alters the performance based on threshold value by dividing the image into isolated, heterogeneous and homogeneous points [37], [39]....

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  • ...Kaun Filter: Based on adaptive speckle model and it is a systematic derivation of Lee filter with extensions, multiplicative model is converted into additive form [37], [30]....

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