SRAD Filter Research Articles

Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images

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.

Center affine filter based adaptive image despeckling method with preserving details

Speckle noises inherently exist in radar, sonar, ultrasound and optical coherent tomography images. Such noises greatly degrade image quality, and thus make the tasks such as object detection and image registration difficult. With the Center Affine Filter (CAF), this paper proposes a new adaptive method for suppressing speckle noises. The proposed method adopts both local arithmetic mean and local geometric mean around an image pixel to compute the dispersion parameter of the CAF. The metrics of arithmetic and geometric mean not only intimately relate to the probability density function of speckle noise, but also reflect the image homogeneity; therefore, the designed CAF is capable of adaptively suppressing the speckle noises while maintaining the useful details in an image. Then, to speed up the proposed method, the technique of the integral image is used to get the local arithmetic and local geometric mean. The proposed method is experimentally tested on both synthetic and real images, which shows a prospective performance over Lee filter, SRAD filter, and the state-of-the-art BNLM filter.

Sonar image segmentation and quality assessment using prominent image processing techniques

Sound navigation and ranging (SONAR) is a technique that uses sound propagation to navigate, communicate or to detect objects on or under the surface of the water. It even forecasts what lies beneath the sea waves. It uses sound waves to observe the happenings occurring on the region of seafloor. Sonar images that are framed using sound waves are not directly suitable for analysis as it would be affected primarily by a speckle noise which misguides the way of interpretation. The metaphors of substances in those images are very important in the field of oceanography to suit many applications. The scope of this research work is to promote the quality of an image in order to get a better interpretation than using a raw image. This article also deals with the novel work to identifying the type of noise present in sonar image using an artificial neural network (ANN) and the overall accuracy of the network achieved is found to be 93.37%. In addition, filtration of noise has been done using different digital filters and it is proved that the combination of SRAD and Gaussian filter is best suited for sonar image denoising processes. Finally, this work contributes many ideas related to the segmentation of objects in an image using various techniques like Fuzzy C Means (FCM), K means, Expectation–maximization (EM) algorithm, Otsu thresholding and Toboggan Segmentation. The research outcomes are compared with different quality matrices and it exhibits that the FCM segmentation has high accuracy than the other methods and all the results were validated through MATLAB software.

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

The liver ultrasonic (US) images suffer from inherent speckle noise, degrading the image quality; thereby, affecting human interpretation and also reducing the accuracy in computer-assisted diagnostic techniques. In this paper, we propose an improved despeckle filtering technique by combining, maximum homogeneity over a pixel neighborhood filtering (MHON) and speckle-reducing anisotropic diffusion filtering (SRAD) using a binary classifier map (BCM). The textural and fine details of residual image are denoised by SRAD filtering. The SRAD filter denoised image and MHON filter denoised image combines to attain BCM. A BCM is generated by examining the local coefficient of dispersion (CoD) assessed using a kernel with the one obtained from a specific region. The proposed method is evaluated on clinical US image set of liver, by assessing the quality factors PSNR, SNR, ISC, LMSE and EPI. Results are compared with specified algorithms separately while accomplishing for proposed method.

The effect of filtering algorithms for breast ultrasound lesions segmentation

Abstract Breast ultrasound images have a complicated structure, which is difficult to be segmented due to the fact that it has low signal and affected by noise ratio. Recent research concentrated on the Region of Interest (ROI) labeling and ROI segmentation. In order to reduce chances of human error, stages of processing in breast ultrasound images might be different from one another. This research proposes a new image filtering method for breast ultrasound, namely Altered Phase Preserving Dynamic Range Compression (APPDRC). In addition, this paper compares the performance of filtering algorithms, in combination with standard thresholding segmentation. Focusing on the filtering stage, a comparison between the proposed method APPDRC Filter and previous approaches is validated on a dataset of 306 images, namely Inverted Median filter, Multifractal Filter, Hybrid Filter, SRAD filter, and PPDRC. Further, a summary of the work to date on the effect of filtering on lesion segmentation in ultrasound breast images is reported. Jaccard Similarity Index (JSI) is used for evaluation, in which the automated segmentation result is compared with the experienced radiologist's manual delineation. Our results revealed that making the choice of filtering algorithm affects the final segmentation results. Considering Mean JSI, Dice and MCC metrics, the proposed APPDRC Filter achieved the best performance, and outperformed the five evaluated filtering methods.

A Novel Algorithm to Select a Seed Point Automatically In Breast Ultrasound Image

In automatic segmentation of breast ultrasound images, seed point is to be selected automatically for contour evolution to proceed. The total computation task involved in the initialization of seed point, occupies most of the system time and resource. In this paper, the proposed method computes and identifies the seed point automatically by which automatic contour initialization is done. In pre-processing stage Improved SRAD filter is developed and method for selection of seed point automatically is based on the changes in gray level intensities or texture features, which serves as an initialization for level set segmentation. The seed point that is automatically plotted on to Ultrasound B-scan images also plotted on its corresponding elastogram pair.Proposed method is applied on to 50 US B scan images of benign solid mass, 80 malignant solid massed and 30 images of complex and simple cysts. For validation process, the seed point obtained by the approach mapped on the image pairs. The above approach shows us that the work presented will successfully and seed point using texture values of US B scan Images with an accuracy of 87%. With this proposed method the computational time and resource used for segmentation of Breast legions will be minimized to maximum extent and is more accurate.

Fundus image denoising using FPGA hardware architecture

Image processing algorithms, implemented in hardware, have recently emerged as the most viable solution for improving the performance of image processing systems. In this paper, a version of an anisotropic diffusion technique is used to reduce noise from retinal images, namely Speckle Reducing Anisotropic Diffusion SRAD. The SRAD filter can improve images corrupted by multiplicative or additive noise, but it has been the most computationally complex and it has not been suitable for software implementation in real-time processing. In this paper, an efficient Field-Programmable Gate Array FPGA-based implementation of the SRAD filter is presented to accelerate the processing time. A comparison of the most used classical suppression filters like Gaussian, Median, Perona and Malik anisotropic diffusion has been carried out. The experimental results reveal a 38× performance improvement over the original MATLAB implementation and a 1.33× performance improvement over the hardware implementation using the Xilinx System Generator tool.

A Basic Study on Improving Ultrasound Images

This proposed study method involves the implementation of Frost filter, Lee filter and SRAD filter on ultrasound images and a quantitative evaluation of peak signal-to-noise ratio (PSNR) and Pratt’s figure of merit (FOM).

De-Speckling by Hybridization of Wavelet Transform and Bilateral Filter in Ultrasound Images

In this paper we introduce a de-noising technique with hybridization of wavelet transform and bilateral filter. In digital image processing, image is corrupted by various types of noise. But medical images are usually corrupted by speckle noise. So to remove noise, image de-speckling is very essential exercise of diagnose. To speckle noise reduction, multiplicative noise is converted into additive noise. Various filters are implemented for the reduction of speckle noise from ultrasound images. The proposed method uses wavelet transform and bilateral filter to reduce speckle noise from ultrasound images with homomorphic approach. We compare our results with mean filter, median filter, Gaussian filter, bilateral filter, wiener filter, Lee filter, Kuan filter and SRAD filters. So, we use bilateral filter using homomorphic approach with wavelet thresholding. The use of Weiner filter and soft thresholding provides better results to reduce speckle noise and gives better image quality with fine details. Another method is developed for mixed noise reduction from ultrasound images. We compare the results of proposed methods with the existing approaches in terms of PSNR and MSE. The hybridization of wavelet transforms and bilateral filter has been shown work well than other smoothing filters and mixed noise reduction methods.

A fourth-order PDE-based non-linear filter for speckle reduction from Optical Coherence Tomography images

In this paper, a fourth-order PDE-based non-linear filter for speckle reduction from Optical Coherence Tomography (OCT) images is proposed in a variational framework. The speckle noise pattern in OCT images is distributed according to Rayleigh’s Probability Distribution Function (pdf). The initial condition of the proposed PDE-based filter is the speckle noised OCT images and the output is the better quality speckle reduced image. For digital implementations, the proposed scheme is discretised using the finite difference scheme. The performance comparison of the proposed scheme with other standard speckle reduction techniques such as the Lee filter, the Frost filter, the Kuan filter, the SRAD filter, the homomorphic Wiener filter and homomorphic versions of the anisotropic diffusion based filter, the non-linear complex diffusion-based filter and the fourth order PDE-based filter is also presented. The obtained results justify the applicability of the proposed scheme.

Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The main problem during diagnosis is the distortion of visual signals obtained which is due to the consequence of the coherent of nature of the wave transmitted. These distortions are termed as ‘Speckle Noise’. The present study focuses on proposing a technique to reduce speckle noise from ultrasonic devices. This technique uses a hybrid model that combines fourth order PDE based anisotropic diffusion, linked with SRAD filter and wavelet based BayesShrink technique. The proposed filter is compared with traditional filters and existing filters using anisotropic diffusion. Experimental results prove that the proposed method is efficient in reaching convergence quickly and producing quality denoised images.

Despeckling of Oil Spill SAR Images using Fusion Technique

speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the oil spill images and makes interpretations, analysis and classifications of SAR images harder. Therefore, some speckle mitigation is necessary prior to the processing of SAR oil spill images. In this paper, some basic speckle reduction filters like Kuan, Lee, Frost, antistrophic diffusion and SRAD filters are used. A New method is proposed for despeckling of SAR oil spill images which combines the frost filter with relaxed median filter. The proposed method gives better results when compared to other methods in terms of statistical parameters like PSNR, MSE, energy and entropy value. The approach can also reduce the computing time compared with other approaches.

A versatile technique for visual enhancement of medical ultrasound images

The paper presents a versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD). Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and GNDShrink have been implemented and evaluated on both the artificial speckle simulated images and real US images. The experimental results demonstrate the superiority of the suggested technique both quantitatively and qualitatively as compared to other competitive schemes reported in the image denoising literature, e.g., the proposed method yields a gain of more than 0.36 dB over the best state-of-the-art despeckling method (GenLik), 0.93 dB over SRAD filter, 2.35 dB over Lee filter, and 1.34 dB over Kuan filter in terms of signal-to-noise ratio, when tested on the realistic US images. The visual comparison of despeckled US images and the higher values of quality metrics (coefficient of correlation, edge preservation index, quality index, and structural similarity index) indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces. Further, the proposed method will be evaluated on other class of images as well as by employing multiple observer evaluation.