Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering
26 Jul 2013-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 73, Iss: 4, pp 1-7
TL;DR: 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.
Abstract: 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.
Citations
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01 Feb 2018
TL;DR: This paper investigates the performance efficiency of a newly developed Synergistic Fibroblast optimization based Weighted Median Filter (SFO-WMF) for medical image analysis and demonstrates that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.
Abstract: Image analysis involves processing and extraction of knowledge from images which is significantly useful in the large scale of medical and engineering applications. Image denoising is primarily applied in image analysis for degradation of noise, and thus improves the visual quality of images for information retrieval process. In the recent scenarios, due to the increase of complexity and diversity of digital images, removal of noise present in complicated images using classical filters becomes a quite challenge. The objective of this paper is to investigate the performance efficiency of a newly developed Synergistic Fibroblast optimization based Weighted Median Filter (SFO-WMF) for medical image analysis. Experiments are carried out with benchmark images, real time Magnetic Resonance Imaging (MRI) images, ultrasound breast cancer images and compared with conventional filters, namely, mean filter, median filter, wiener filter, Gaussian filter and weighted median filter. The performances of filters are validated using standard performance metrics and computational results demonstrated that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.
3 citations
Cites methods from "Wavelet based Despeckling of Medica..."
...The proposed method was evaluated with synthetically speckled ultrasound image dataset and the obtained results demonstrated its effectiveness [10]....
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References
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TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
12,560 citations
"Wavelet based Despeckling of Medica..." refers methods in this paper
...[13] P. Perona and J. Malik....
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...For speckle reduction, Yu and Acton [20] proposed an improved approach of anisotropic diffusion concept proposed by Perona and Malik [13]....
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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.
2,701 citations
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.
1,906 citations
"Wavelet based Despeckling of Medica..." refers methods in this paper
...The conventional methods described in [10], [5] and [9] often oversmooths the important details while reducing the speckle noise....
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TL;DR: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications, and validates the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery.
Abstract: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.
1,816 citations
TL;DR: The adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance and its easy extension to deal with various types of signal-dependent noise.
Abstract: In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.
1,475 citations
"Wavelet based Despeckling of Medica..." refers methods in this paper
...The conventional methods described in [10], [5] and [9] often oversmooths the important details while reducing the speckle noise....
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