Noise Reduction Filter Research Articles

A comparative study of three speckle reducing methods for intima-media thickness ultrasound images.

Background:Ultrasonic evaluation of intima-media thickness (IMT) is an early marker of assessing the development of atherosclerosis and determining cardiovascular risk. To attain the best possible diagnosis, it is essential that medical images be clear, sharp and without noise and artifacts.Objectives:Comparison of speckle reducing anisotropic diffusion (SRAD), discrete (DTD) and continuum topological derivative (CTD) on B-mode ultrasound images of common carotid and brachial arteries throughout the cardiac cycle.Patients and Methods:In a cross-sectional design, an examination was performed on forty-two human subjects with a mean age of 44 ± 6 years from April 2013 to June 2013. This study was approved by the ethics committees of Kashan University of Medical Sciences and Beheshti Hospital. An ultrasonic examination of common carotid and brachial arteries of forty-two human subjects was performed. The program was designed in MATLAB software to extract consecutive B-mode images and apply region of interest (ROI) on the IMT of the common carotid and brachial arteries. Then, three different noise reduction filters with the Canny edge detection were used in ROI separately. Finally, the program measured the image quality metrics.Results:According to values of eleven different image quality metrics (mentioned in the main text), there was a significant difference between CTD, DTD and SRAD filters with the Canny edge detection status in the common carotid and brachial arteries throughout the cardiac cycle (all P values < 0.001). For example, peak signal to noise ratios (PSNR) using CTD, DTD and SRAD filters were 95.43 ± 0.64, 88.86 ± 0.82 and 73.02 ± 0.20 in common carotid and 96.39 ± 1.25, 92.58 ± 0.11 and 88.27 ± 0.63 in brachial arteries, respectively (both P values < 0.001).Conclusions:By measuring image quality metrics, this study showed that DTD and CTD filters with the Canny edge detection respectively, are better than SRAD filter with the Canny detection for speckle suppression and details preservation in both arteries in the ultrasound images.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.

Adaptive Fuzzy Switching Noise Reduction Filter for Iris Pattern Recognition

Noise reduction is a necessary procedure for the iris recognition systems. This paper proposes an adaptive fuzzy switching noise reduction (AFSNR) filter to reduce noise for iris pattern recognition. The proposed low complexity AFSNR filter removes noise pixels by fuzzy switching between an adaptive median filter and the filling method. The threshold values of AFSNR filter are calculated on the basis of the histogram statistics of eyelashes, pupils, eyelids, and light illumination. The experimental results on the CASIA V3.0 iris database, with genuine acceptance rate equals 99.72%, show the success of the proposed method.

Preconditioning seismic data for channel detection

Random seismic noise, present in all 3D seismic data sets, hampers manual interpretation by geoscientists and automatic analysis by a computer program. As a result, many noise-suppression techniques have been developed to enhance image quality. Accurately suppressing seismic noise without damaging image details is crucial in preserving small-scale geologic features for channel detection. The automatic detection of channel patterns theoretically should be easy because of their unique spatial signatures and scales, which differentiate them from other common 3D geobodies. For example, one notable channel characteristic has high local linearity: Spatial coherency is much greater in one direction than in other directions. A variety of techniques, such as spatial filters, can be used to enhance this “slender” channel feature in areas of high signal-to-noise ratio (S/N). Unfortunately, these spatial filters may also reduce the edge detectability in areas of low S/N. In this paper, I compared the effectiveness of three noise reduction filters: (1) running average, (2) redundant wavelet transform (RWT), and (3) polynomial fitting. I demonstrated the usefulness of these filters prior to edge detection to enhance channel patterns in seismic data collected from Saudi Arabia. The data examples demonstrated that RWT and polynomial fitting can successfully preserve, enhance, and delineate channel edges that were not visible in conventional seismic amplitude displays, whereas the running average filter further smeared the detectability of channel edges.

Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography.

We developed robust, three-dimensional methods, as opposed to traditional A-line analysis, for estimating the optical properties of calcified, fibrotic, and lipid atherosclerotic plaques from in vivo coronary artery intravascular optical coherence tomography clinical pullbacks. We estimated attenuation [Formula: see text] and backscattered intensity [Formula: see text] from small volumes of interest annotated by experts in 35 pullbacks. Some results were as follows: noise reduction filtering was desirable, parallel line (PL) methods outperformed individual line methods, root mean square error was the best goodness-of-fit, and [Formula: see text]-trimmed PL ([Formula: see text]-T-PL) was the best overall method. Estimates of [Formula: see text] were calcified ([Formula: see text]), fibrotic ([Formula: see text]), and lipid ([Formula: see text]), similar to those in the literature, and tissue classification from optical properties alone was promising.

A Family of Maximum SNR Filters for Noise Reduction

This paper is devoted to the study and analysis of the maximum signal-to-noise ratio (SNR) filters for noise reduction both in the time and short-time Fourier transform (STFT) domains with one single microphone and multiple microphones. In the time domain, we show that the maximum SNR filters can significantly increase the SNR but at the expense of tremendous speech distortion. As a consequence, the speech quality improvement, measured by the perceptual evaluation of speech quality (PESQ) algorithm, is marginal if any, regardless of the number of microphones used. In the STFT domain, the maximum SNR filters are formulated by considering the interframe information in every frequency band. It is found that these filters not only improve the SNR, but also improve the speech quality significantly. As the number of input channels increases so is the gain in SNR as well as the speech quality. This demonstrates that the maximum SNR filters, particularly the multichannel ones, in the STFT domain may be of great practical value.

English

Noise removal from images is a part of image restoration in which we try to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Noises present in images can be of various types with their characteristic Probability Distribution Functions (PDF). Noise removal techniques depend on the kind of noise present in the image rather than on the image itself. This paper explores the effects of applying noise reduction filters having similar properties on noisy images with emphasis on Signal-to-Noise-Ratio (SNR) value estimation for comparing the results.

Reduction in impulse noise in digital images through a new adaptive artificial neural network model

In this paper, an adaptive artificial neural network model is developed in order to restore severely corrupted images. The proposed new and effective impulse noise reduction filter is named as adaptive neural network models with an algorithm based on artificial neural networks. Networks trained at different noise intensities get activated according to the intensity of the noise and estimate the most suitable neighboring pixel that can replace the corrupted pixel. The proposed algorithm reduces impulse noise effectively while also protecting the details. Experimental results show that the proposed algorithm performs better compared with other traditional filters.

Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals.

Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.

Improving velocity feedback for position control by using a discrete-time sliding mode filtering with adaptive windowing

In position control of mechatronic devices, velocity feedback is important for injecting additional damping to avoid low-frequency fluctuation around desired trajectories. In practice, velocity signal is often obtained by finite difference of position signal from an optical encoder. However, such a numerical differentiation produces high-frequency noise by magnifying quantization error contained in the position signal. As a result, the controller may produce high-frequency vibration. This paper presents a new noise-reduction discrete-time filter based on sliding mode and adaptive windowing. The presented filter is an improved version of a sliding mode filter by Jin et al. (2012), with including adaptive windowing of which the window size is determined in a similar way to that of a discrete-time adaptive windowing differentiator by Janabi-Sharifi et al. (2000). The presented filter is then applied to a position control of a mechatronic device for improving velocity feedback. Experimental results show that the presented filter provides better velocity feedback than its previous version, Janabi-Sharifi et al.’s differentiator, and combinations of these two filters.

Automatic Event Detection in Noisy Environment for Material Process Monitoring by Laser AE Method

Laser acoustic emission (AE) method is a unique in-situ and non-contact nondestructive evaluation (NDE) method. It has a capability to detect signals generated from crack generation and propagation, friction and other physical phenomena in materials even in high temperature environment. However, laser AE system has lower signal-to-noise ratio compared to the conventional AE system using PZT sensors, so it is difficult to apply this method in noisy environment. A novel AE measurement system to detect events in such difficult environments was developed. This system could continuously record all AE waveforms and enable unrestricted post-analyses. Noise reduction filters in frequency domain coupling with a new AE event extraction using multiple threshold values showed a good potential for AE signal processing. This system was successfully applied for crack monitoring of plasma spray deposition process of ceramic coating.

A novel hybrid homomorphic fuzzy filter for speckle noise reduction

Purpose The presence of speckle noise and artifacts make delineation of transthoracic echocardiographic (TTE) images quite difficult and challenging; the edges are to be preserved while removing noise. To address issues, a novel speckle reduction technique is being proposed and analyzed.

P‐55: Adaptive Noise Reduction Method using Variable Window Size Based on Region Analysis

AbstractThis paper proposes a novel denoising method which uses a variable window size for noise filter depending on the results of local region analysis. The proposed method classifies local regions of a given noisy image as detail or smooth regions using a new parameter which is calculated by the ratio of estimated noise variance to local variance. The new parameter, a detail descriptor, represents the amount of image details in a local region. Then, the proposed method adaptively applies a noise reduction filter to remove image noise while preserving image details by regulating the filtering window size depending on the detail descriptor.

Detection of segmentation cracks in top coat of thermal barrier coatings during plasma spraying by non-contact acoustic emission method

Numerous cracks can be observed in the top coat of thermal barrier coatings (TBCs) deposited by the atmospheric plasma spraying (APS) method. These cracks can be classified into vertical and horizontal ones and they have opposite impact on the properties of TBCs. Vertical cracks reduce the residual stress in the top coat and provide strain tolerance. On the contrary, horizontal cracks trigger delamination of the top coat. However, monitoring methods of cracks generation during APS are rare even though they are strongly desired. Therefore, an in situ, non-contact and non-destructive evaluation method for this objective was developed in this study with the laser acoustic emission (AE) technique by using laser interferometers as a sensor. More AE events could be detected by introducing an improved noise reduction filter and AE event detection procedures with multiple thresholds. Generation of vertical cracks was successfully separated from horizontal cracks by a newly introduced scanning pattern of a plasma torch. Thus, generation of vertical cracks was detected with certainty by this monitoring method because AE events were detected only during spraying and a positive correlation was observed between the development degree of vertical cracks and the total AE energy in one experiment.

MO‐G‐17A‐03: MR‐Based Cortical Bone Segmentation for PET Attenuation Correction with a Non‐UTE 3D Fast GRE Sequence

Purpose:To determine the feasibility of identifying cortical bone on MR images with a short‐TE 3D fast‐GRE sequence for attenuation correction of PET data in PET/MR.Methods:A water‐fat‐bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE1/TE2/TE3=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast‐GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm3) was implemented along with a 3D Dixon sequence (TE1/TE2/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm3) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm3) and reconstructed voxel size (0.94×0.94×1.5mm3) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast‐GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram.Results:There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short‐TE MR image. The acquisition time for the 3D fast‐GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water‐fat‐bone segmentation is visually similar compared to reference CT.Conclusion:Cortical bone and air can be separated based on intensity in MR image with a short‐TE 3D fast‐GRE sequence. Further research is required to optimize the strategy to reduce Gibbs artifacts.

TU-F-18C-03: X-Ray Scatter Correction in Breast CT: Advances and Patient Testing

Purpose: To further develop and perform patient testing of an x-ray scatter correction algorithm for dedicated breast computed tomography (BCT). Methods: A previously proposed algorithm for x-ray scatter signal reduction in BCT imaging was modified and tested with a phantom and on patients. A wireless electronic positioner system was designed and added to the BCT system that positions a tungsten plate in and out of the x-ray beam. The interpolation used by the algorithm was replaced with a radial basis function-based algorithm, with automated exclusion of non-valid sampled points due to patient motion or other factors. A 3D adaptive noise reduction filter was also introduced to reduce the impact of scatter quantum noise post-reconstruction. The impact on image quality of the improved algorithm was evaluated using a breast phantom and seven patient breasts, using quantitative metrics such signal difference (SD) and signal difference-to-noise ratios (SDNR) and qualitatively using image profiles. Results: The improvements in the algorithm resulted in a more robust interpolation step, with no introduction of image artifacts, especially at the imaged object boundaries, which was an issue in the previous implementation. Qualitative evaluation of the reconstructed slices and corresponding profiles show excellent homogeneity of both the background andmore » the higher density features throughout the whole imaged object, as well as increased accuracy in the Hounsfield Units (HU) values of the tissues. Profiles also demonstrate substantial increase in both SD and SDNR between glandular and adipose regions compared to both the uncorrected and system-corrected images. Conclusion: The improved scatter correction algorithm can be reliably used during patient BCT acquisitions with no introduction of artifacts, resulting in substantial improvement in image quality. Its impact on actual clinical performance needs to be evaluated in the future. Research Agreement, Koning Corp., Hologic Inc., Barco Corp Consultant Agreement, Fujifilm Medical Systems.« less

DORSAL HAND VEIN PATTERN AUTHENTICATION BY HOUGH PEAKS

The quest of providing more secure identification system has led to rise in developing biometric systems. Biometrics such as face, fingerprint and iris have been developed extensively for human identification purpose and also to provide authentic input to many security systems in the past few decades. Dorsal hand vein pattern is an emerging biometric which is unique to every individual. In this paper Linear Hough transform is used to extract the features of query and data base images. K-Nearest neighbor Search is used to obtain best match between query image and database. The extraction of the vein patterns was obtained by morphological techniques. Noise reduction filters are used to enhance the vein patterns.

Multichannel Noise Reduction in the Karhunen-Loève Expansion Domain

The noise reduction problem is traditionally approached in the time, frequency, or transform domain. Having a signal dependent transform has shown some advantages over the traditional signal independent transform. Recently, the single-channel noise reduction problem in the Karhunen-Loeve expansion (KLE) domain has received special attention. In this paper, the noise reduction problem in the KLE domain is studied from a multichannel perspective. We present a new formulation of the problem, in which inter-channel and inter-mode correlations are optimally exploited. We derive different optimal noise reduction filters and present a set of useful performance measures within this framework. The performance of the different filters is then evaluated through experiments in which not only noise but also competing speech sources are present. It is shown that the proposed multichannel formulation is more robust to competing speech sources than the single-channel approach and that a better compromise between noise reduction and speech distortion can be obtained.

An Improved Fractional Fourier Transform Based Reconfigurable Filter Bank for Hearing Aid

The problem of reducing noise in hearing aids is one of great importance and great difficulty. The problem has been addressed in many different ways over the years. The techniques used range from relatively simple forms of filtering to advanced signal processing methods. Filter banks for digital hearing aids must use significantly different criteria than those designed for coding applications. For digital hearing aids, the filter bank channel gains must be adjustable over a large dynamic range to compensate for the hearingloss. This paper presents a filter bank designed exclusively for hearing aid applications. Here we propose a fractional Fourier transform filter for reduction of noise in hearing aids. Our method enables hearing- impaired people to customize hearing aids based on their own specific conditions to improve their hearing ability. An experimental result is evaluated and shows the proposed method has high performance when compared with existing methods. The simulation results show that the proposed filter bank achieves good matching between audiograms and filter magnitude responses. The filter delay is significantly reduced compared to that of the other filter bank methods.

Development and application of MIPAR™: a novel software package for two- and three-dimensional microstructural characterization

AbstractThree-dimensional microscopy has become an increasingly popular materials characterization technique. This has resulted in a standardized processing scheme for most datasets. Such a scheme has motivated the development of a robust software package capable of performing each stage of post-acquisition processing and analysis. This software has been termed Materials Image Processing and Automated Reconstruction (MIPAR™). Developed in MATLAB™, but deployable as a standalone cross-platform executable, MIPAR™ leverages the power of MATLAB’s matrix processing algorithms and offers a comprehensive graphical software solution to the multitude of 3D characterization problems. MIPAR™ consists of five modules, three of which (Image Processor, Batch Processor, and 3D Toolbox) are required for full 3D characterization. Each module is dedicated to different stages of 3D data processing: alignment, pre-processing, segmentation, visualization, and quantification.With regard to pre-processing, i.e., the raw-intensity-enhancement steps that aid subsequent segmentation, MIPAR’s Image Processor module includes a host of contrast enhancement and noise reduction filters, one of which offers a unique solution to ion-milling-artifact reduction. In the area of segmentation, a methodology has been developed for the optimization of segmentation algorithm parameters, and graphically integrated into the Image Processor. Additionally, a 3D data structure and complementary user interface has been developed which permits the binary segmentation of complex, multi-phase microstructures. This structure has also permitted the integration of 3D EBSD data processing and visualization tools, along with support of additional algorithms for the fusion of multi-modal datasets. Finally, in the important field of quantification, MIPAR™ offers several direct 3D quantification tools across the global, feature-by-feature, and localized classes.