Enhancement of the ultrasound images by modified anisotropic diffusion method (original) (raw)
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Adaptive anisotropic diffusion filter for speckle noise reduction for ultrasound images
In image processing noise is possibly the most annoying problem. Ultrasound image has been used for effective diagnosis for its non-invasive, harmless, accurate and cost effectiveness. Unfortunately, ultrasound images are degraded by an intrinsic artefact called speckle. Eliminating such speckle noise is an important preprocessing task. For acquiring a better performance for denoising, an adaptive anisotropic diffusion technique for ultrasound images is presented in this paper. It is a new efficient method for denoising the image without blurring the frontiers between different regions. The proposed noise detection-oriented method is based on the differentiation of the diffusion in the direction of the gradient and the adjacent pixels of entire image. The performance of different metrics shows the proposed method exhibit better result to existing Perona-Malik anisotropic diffusion method.
An Efficient Filtering Approach for Speckle Reduction in Ultrasound Images
Biomedical and Pharmacology Journal, 2017
Ultrasound (US) imaging is a valuable imaging technique for clinical diagnosis. It is noninvasive in nature and imaging the internal structure of the body to identify the probabilistic diseases or, abnormalities in tissues behavior. However, inherent response of speckle noise in US images limit the fine and edge details which affect the contrast resolution. This makes clinical diagnosis more difficult. In this paper, we proposed a non-linear anisotropic diffusion filtering for speckle reduction approach based on non-linear progression partial differential equation (PDE). For analysis purpose, we have considered the set of eight-real clinical B-Mode US images of human liver from different patient. These real US images are used for quantitative analysis. We compare the performance of four speckle reduction filters as Perona-Malik Filter, LEE Filter, FROST Filter, ADMBSS Filter with our proposed filter in terms of peak signal to noise ratio (PSNR) value performance index under various noise variance selection Parmenter. Finally, we see that our proposed approach preserves the clinical details in US images and minimizing the noise level. Results for set of eight US images shows that our proposed filtering approach is more efficient for speckle noise reduction in comparison to other discussed filters in term of higher PSNR value (dB).
Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images
Intelligent Automation & Soft Computing
Speckle Reduction Anisotropic Diffusion filter which is used to despeckle ultrasound images, perform well at homogeneous region than in heterogeneous region resulting in loss of information available at the edges. Extended SRAD filter does the same, preserving better the edges in addition, compared to the existing SRAD filter. The proposed Extended SRAD filter includes the intensity of four more neighboring pixels in addition with other four that is meant for SRAD filter operation. So, a total of eight pixels are involved in determining the intensity of a single pixel. This improves despeckling performance by maintaining the information accessible at an image's edges. The proposed filter produces better Peak Signal to Noise Ratio, Root Mean Square Error and Structural Similarity Index values for standard test images with different noise levels with variance 0.3, 0.35 and 0.4. It also performs well in denoising breast ultrasound images at different noise levels.
2012
Abstract Anisotropic diffusion is being widely used in reducing speckle noise of ultrasound images. However, the traditional anisotropic diffusion algorithms are poor at preserving edges and usually make the image edges blurred when denoising, which negatively affects the following image analysis. In this paper, we modify the standard speckle reducing anisotropic diffusion to increase its ability of detecting edges and suppress the smooth at edge by using the separability of images.
Biomedical Ultrasound Image Enhancement using SRAD
2013
In this paper we present Speckle Reducing Anisotropic Diffusion (SRAD) technique that uses wavelet decomposition. This technique is able to preserve and enhance edges while smoothing homogeneous regions in ultrasound images. SRAD is applied on various real biomedical ultrasound images with different number of iterations. The performance of SRAD filter is found to be much better than conventional Lee, Frost filters. SRAD gives less MSE, higher PSNR and better FOM. The experiemental results show that this technique works effectively both in terms of speckle reduction, edge preservation and edge enhancement. General Terms Techniques, Experiements.
Implementation of Cost Efficient Image Enhancement Technique Reduce Speckle in Ultrasound Images
Speckle is a granular multiplicative noise that reduces the resolution and contrast of the image there by degrading the diagnostic accuracy of the Ultrasound image. Speckle reduction technique has to be followed to enhance the quality of ultrasound image [3].Speckle noise occurs in all coherent imaging systems, such as ultrasound images. The speckle noise in ultrasound images is often considered as undesirable and has a negative impact on clinical practitioners for diagnosis. Because of the signal-dependent nature of the speckle intensity, speckle noise in ultrasound imaging requires specific handling. So, any ultrasound speckle de-noising method must be designed in such a way that the speckle noise be suppressed without smearing the edges. In other words, any speckle de-noising method must preserve both the edges and structural details of the image and its quality [8].Digital image enhancement techniques are to improving the visual quality of images. Main objective of image enhancement is to process an image so that result is more suitable than original image for specific application. This paper presents real time hardware image enhancement techniques using field programmable gate array (FPGA) [10].It presents architecture for filters pixel by pixel and regions filters for image processing using Xilinx System Generator (XSG). This architecture offer an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explore important aspects concerned to hardware implementation
Study on the Reduction of Speckle Noise in Ultrasound Images
The ultrasound images, the speckle noise is inherent in medical ultrasound images and it is the cause of reduced contrast-to-noise ratio and resolution. The presence of speckle noise is not attractive because it reduces the image quality and affects the tasks of the individual interpretation and paper diagnosis. In this paper, we study various techniques to reduce speckle noise from ultrasound images Post acquisition method as the only scale spatial filtering method and multi-scale method.
A Comparison of Speckle Reduction Techniques in Medical Ultrasound Imaging
Applied Medical Informatics, 2015
Speckle noise is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In addition, it limits the efficient application of intelligent image processing algorithms, such as segmentation techniques. Thus, speckle noise reduction is considered an essential pre-processing step. The objective of this paper is to carry out a comparative evaluation of speckle filtering techniques, based on two image quality evaluation metrics, the Peak Signal to Noise Ratio (PSNR), and the Structural SIMilarity (SSIM) index, and visual evaluation.
Speckle Noise Reduction in Medical Ultrasound Images
ArXiv, 2013
Ultrasound imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. In this paper, we propose a denoising approach which combines logarithmic transformation and a non linear diffusion tensor. Since speckle noise is multiplicative and nonwhite process, the logarithmic transformation is a reasonable choice to convert signaldependent or pure multiplicative noise to an additive one. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image. To illustrate the effective performance of our algorithm, we present some experimental results on synthetically and...