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Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full‐reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full‐reference and three no‐reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second‐best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre‐processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies. [ABSTRACT FROM AUTHOR]

This study presents a geometric deformable model‐based segmentation approach to segmentation of the intima and media‐adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre‐processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern‐based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were r = 0.782, r = 0.716, r = 0.956, and r = 0.874 for the 20 MHz images, respectively, and r = 0.990, r = 0.995, r = 0.989, and r = 0.996 for the 45 MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at r > 0.967 and r > 0.993 for 20 and 45 MHz images, respectively. [ABSTRACT FROM AUTHOR]

Non‐local self‐similar patch‐based denoising techniques have been viewed as the most popular denoising approaches in computer vision. This study has proposed a novel iterative weighted sparse representation (IWSR) scheme for X‐ray cardiovascular angiogram image denoising. The main procedures of this scheme include four parts. First, a maximum a posterior (MAP) distribution by the Bayes' theory is adopted to simultaneously estimate the estimated image and sparse representation with different Gaussian distributions approximating to likelihood prior, non‐local self‐similar patch prior and sparse representation prior. Second, the MAP problem is converted to minimise an energy function using the logarithmic transformation. Third, the function is efficiently solved by the single and effective alternating directions method of multipliers algorithm along with singular value decomposition (SVD) algorithm. Finally, owing to learned dictionary by K‐SVD algorithm, the qualitative and quantitative results of widely synthetic experiments demonstrate that the proposed IWSR denoising method performs effectively and can obtain competitive denoising performance and high‐quality images compared with those advanced denoising methods. The results of extensive experiments on clinical X‐ray angiogram images further illustrate that the IWSR method performs well on noise reduction and vascular structures including edges and capillaries preservation, integral cardiovascular trees of which are beneficial for clinicians to diagnose and analyse cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Purpose: Recent efforts in the reconstruction of interventional devices from two distinct views require the segmentation of the object in both fluoroscopic images. Noise might decrease the quality of the segmentation and cause artifacts in the reconstruction. The noise level depends on the x-ray dose the patient is exposed to. The proposed algorithm reduces the noise and enhances the separability of curvilinear devices in background subtracted fluoroscopic images to allow a more accurate segmentation. Methods: The algorithm uses a set of binary masks to estimate a line conformity measure that determines the best direction for a directional filter kernel. If the calculated value exceeds a certain threshold, the directional kernel is used to obtain the filtered value. Otherwise, an isotropic filter kernel is used. Results: The evaluation was performed on a set of 36 fluoroscopic images using a vascular head phantom with three different guidewires and nine different x-ray dosages from 6 nGy/pulse to 45 nGy/pulse as well as a clinical data set containing ten images. Compared with wavelet shrinkage and the bilateral filter, the proposed algorithm increased the average contrast to noise ratio by at least 17.8% for the phantom and 68.9% for the clinical images. The accuracy of the device segmentation was improved on average by at least 17.3% and 14.0%, respectively. Conclusions: The proposed algorithm was able to significantly reduce the amount of noise in the images and therefore increase the quality of the device segmentations compared to both the bilateral filter and the wavelet thresholding approach for all acquired noise levels using rotating directional filter kernels near line structures and isotropic kernels for the background. The application of the proposed algorithm for the 3D reconstruction of curvilinear devices from two views would allow a more accurate reconstruction of the device. [ABSTRACT FROM AUTHOR]

Wiener filter yields the minimum‐mean‐square error between the restored image and the original image. However, to obtain an optimal result, there must be accurate knowledge of the power spectra of the noise and the original image besides the degradation function. Otherwise, it will lead to an undesirable restored result. This study presents a so‐called blind‐Wiener filter that can restore the original image when we have no knowledge of the power spectra of both noise and original image. It uses the fact that averaging several consecutively measured images together will enhance signal‐to‐noise ratio (SNR). The number of images to be averaged to reduce noise to an acceptable level was concluded to be around ten. Ten independent random noises were added to a given corrupted image, resulting in ten images with different noises and then each of them was restored by Wiener filter to yield ten Wiener filtered images. Finally, the corrupted image was restored by taking an average over the ten Wiener filtered images. Experiments were conducted in a practical setting to demonstrate the effectiveness of the proposed method. The experimental results show that all the images in the test set were vastly improved and some images gave almost comparable performance to the traditional Wiener filter known as the best restoration method in terms of peak SNR. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) appropriate image processing in conjunction with noise reduction is crucial in proper image segmentation. The authors present a new algorithm, logarithmic adaptive soft morphological (LASM) filter, utilising collectivity and flexibility of order‐statistic soft morphological filters. This method not only reduces the speckle noise of the single‐look SAR imagery considerably, but it significantly enhances the segmentation results. To verify the performance, a simulated SAR image is first created by applying an imagery method to an original noiseless image. The resulting image has characteristics identical to a real SAR image. The LASM method, as well as several well‐known and state‐of‐the‐art filtering algorithms, is then applied to the image to filter out the speckles. The LASM filtered results are then compared with other results by evaluating them against the original image. Some of these methods are also applied to a real SAR image and the results are also compared. Segmentation is then applied to the filtered images and the results are used as a deterministic evaluation metric. The results show marked improvement using the LASM processing the amplitude images compared with well‐known methods, whereas the superiority of LASM algorithm is even more pronounced in processing the intensity images. [ABSTRACT FROM AUTHOR]

Purpose:The authors describe modifications to previously developed cascaded systems analysis to include the anatomical noise in evaluation of dual-energy noise reduction techniques. Previous models have ignored the anatomical noise in theoretical analysis of noise reduction techniques. The inclusion of anatomical noise leads to more accurate estimation of potential noise reduction improvements and optimization. Methods:The model is applied to dual-energy contrast-enhanced mammography. The effect of linear noise reduction filters on the anatomical noise is taken into account using cascaded systems analysis. The noise model is included in the ideal observer detectability for performance evaluation of the noise reduction techniques. Results:Dual-energy image noise with and without including the effect of anatomical noise in noise reduction technique analysis is reported. The theoretical model is compared with clinical images from a previous dual-energy contrast enhanced mammography clinical study and good agreement is observed. The results suggest that the inclusion of anatomical noise in the evaluation and comparison of noise reduction techniques is highly warranted for more accurate analysis. Conclusions:This work establishes a useful extension to dual-energy cascaded systems analysis for maximizing image quality using noise reduction techniques. The extension includes the effect of linear image filtering, such as that used for noise reduction, on anatomical noise. The results suggest that the inclusion of anatomical noise in the evaluation of noise reduction techniques can lead to more accurate optimization, noise, and performance estimations. [ABSTRACT FROM AUTHOR]

Chaotic multi‐dimensional time series (MDTS) exist in some fields such as stock markets and life sciences. To effectively extract the desired information from the measured MDTS, it is important to preprocess data to reduce noise. On the basis of dictionary learning, a method to remove noise is proposed, and the proposed approach is shown to be very effective in the case of MDTS. An MDTS is first considered as a whole, namely an image, and then the method is applied on it. Compared with traditional methods, the proposed approach can utilise the information among the different dimensional time series to improve noise reduction. Using the Lorenz data superimposed by the Gaussian noise as an example, the simulation results have validated the mathematical framework and the performance. [ABSTRACT FROM AUTHOR]