Hybrid Median Filter Research Articles

Comparative Study on Filter Methods of Medical Ultrasound Images

This paper presents a comparative study on six despeckling methods such as modified hybrid median filter, gabor filter, speckle reducing anisotropic diffusion, homomorphic filter, non-local mean filter and squeeze box filter. We select eight objective evaluation parameters, such as signal-to-ratio, contrast signal–to–noise ratio, figure of merit, least absolute error, peak signal-to-noise ratio, edge protection factor, quantitative parameters of despeckling, signal-to-minimum mean square error ratio, to quantify the performance of these filters. The comparative study will provide a good guidance for selecting a suitable filter in the ultrasound image processing.

Hybrid Median Filter Background Estimator for Correcting Distortions in Microtiter Plate Data

Microtiter plate (MTP) assays often exhibit distortions, such as caused by edge-dependent drying and robotic fluid handling variation. Distortions vary by assay system but can have both systematic patterns (predictable from plate to plate) and random (sporadic and unpredictable) components. Random errors can be especially difficult to resolve by assay optimization alone, and postassay algorithms reported to date have smoothing effects that often blunt hits. We implemented a 5 x 5 bidirectional hybrid median filter (HMF) as a local background estimator to scale each data point to the MTP global background median and compared it with a recently described Discrete Fourier Transform (DFT) technique for correcting errors on computationally and experimentally generated MTP datasets. Experimental data were generated from a 384-well format fluorescent bioassay using cells engineered to express eGFP and DsRED. MTP arrays were produced with and without control treatments used to simulate hits in random wells. The HMF demonstrated the greatest improvements in MTP coefficients of variation and dynamic range (defined by the ratio of average hit amplitude to standard deviation, SD) for all synthetic and experimental MTPs examined. After HMF application to a MTP of eGFP signal from mouse insulinoma (MIN6) cells obtained by a plate-reader, the assay coefficient of variation (CV) decreased from 8.0% in the raw dataset to 5.1% and the hit amplitudes were reduced by only 1% while the DFT method increased the CV by 36.0% and reduced the hit amplitude by 21%. Thus, our results show that the bidirectional HMF provides superior corrections of MTP data distortions while at the same time preserving hit amplitudes and improving dynamic range. The software to perform hybrid median filter MTP corrections is available at http://bccg.burnham.org/HTS/HMF_Download_Page.aspx, password is pbushway.

Sensor Fusion Using a Hybrid Median Filter for Artifact Removal in Intraoperative Heart Rate Monitoring

Intraoperative heart rate is routinely measured independently from the ECG monitor, pulse oximeter, and the invasive blood pressure monitor if available. The presence of artifacts, in one or more of theses signals, especially sustained artifacts, represents a critical challenge for physiological monitoring. When temporal filters are used to suppress sustained artifacts, unwanted delays or signal distortion are often introduced. The aim of this study was to remove artifacts and derive accurate estimates for the heart rate signal by using measurement redundancy. Heart rate measurements from multiple sensors and previous estimates that fall in a short moving window were treated as samples of the same heart rate. A hybrid median filter was used to align these samples into one ordinal series and to select the median as the fused estimate. This method can successfully remove artifacts that are sustained for shorter than half the length of the filter window, or artifacts that are sustained for a longer duration but presented in no more than half of the sensors. The method was tested on both simulated and clinical cases. The performance of the hybrid median filter in the simulated study was compared with that of a two-step estimation process, comprising a threshold-controlled artifact-removal module and a Kalman filter. The estimation accuracy of the hybrid median filter is better than that of the Kalman filter in the presence of artifacts. The hybrid median filter combines the structural and temporal information from two or more sensors and generates a robust estimate of heart rate without requiring strict assumptions about the signal's characteristics. This method is intuitive, computationally simple, and the performance can be easily adjusted. These considerable benefits make this method highly suitable for clinical use.

Repeated median and hybrid filters

Standard median filters preserve abrupt shifts (edges) and remove impulsive noise (outliers) from a constant signal but they deteriorate in trend periods. Finite impulse response median hybrid (FMH) filters are more flexible and also preserve shifts, but they are much more vulnerable to outliers. Application of robust regression, in particular of the repeated median, has been suggested for removing subsequent outliers from a signal with trends. A fast algorithm for updating the repeated median in linear time using quadratic space has been given by Bernholt and Fried (Inform. Process. Lett. 88 (2003) 111). Repeated median hybrid filters are constructed to combine the robustness of the repeated median with the edge preservation of FMH filters. Analytical properties of these filters are investigated and their performance is compared via simulations. An algorithm for updating the repeated median is presented which needs only linear space.

Detection of Wind-Induced Response by Wavelet Transformed GPS Solutions

Global positioning system (GPS) solutions offer direct measurements for the dynamic monitoring of long-span and tall civil structures. Timely and correct interpretation of the solutions is essential to improve monitoring and reduce the number of false alarms. In this article, a new approach has been developed that employs the principal component analysis (PCA) of wavelet transformed GPS solutions to detect out-of-control signals. The methodology consists of prefiltering the original GPS solutions via finite infinite response median hybrid (FMH) filter and applying the PCA to Haar wavelet transform (HWT) of the FMH-filtered results. It has been tested off-line using observation data from a tall building monitoring experiment involving GPS and multisensors. Observation data were collected for about seven days in February 2000, including two strong wind periods. The PCA control chart of the wavelet transformed solutions was found to respond better to the wind periods as compared with that of the “raw” solutions. The proposed method can be applied to monitor wind/earthquake or thermally induced responses in the long-term deformation monitoring of large engineering structures by GPS.

NOISE AND OUTLIER REMOVAL FROM JET ENGINE HEALTH SIGNALS USING WEIGHTED FIR MEDIAN HYBRID FILTERS

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. Typical measurement signals found in most jet engines include low rotor speed, high rotor speed, fuel flow and exhaust gas temperature. Deviations in these measurements from a baseline ‘good’ engine are often called measurement deltas and the health signals used for fault detection, isolation, trending and data mining. Linear filters such as the FIR moving average filter and IIR exponential average filter are used in the industry to remove noise and outliers from the jet engine measurement deltas. However, the use of linear filters can lead to loss of critical features in the signal that can contain information about maintenance and repair events that could be used by fault isolation algorithms to determine engine condition or by data mining algorithms to learn valuable patterns in the data. Non-linear filters such as the median and weighted median hybrid filters offer the opportunity to remove noise and gross outliers from signals while preserving features. In this study, a comparison of traditional linear filters popular in the jet engine industry is made with the median filter and the subfilter weighted FIR median hybrid (SWFMH) filter. Results using simulated data with implanted faults shows that the SWFMH filter results in a noise reduction of over 60 per cent compared to only 20 per cent for FIR filters and 30 per cent for IIR filters. Preprocessing jet engine health signals using the SWFMH filter would greatly improve the accuracy of diagnostic systems.

Data Rectification and Detection of Trend Shifts in Jet Engine Path Measurements Using Median Filters and Fuzzy Logic

Filtering methods are explored for removing noise from data while preserving sharp edges that many indicate a trend shift in gas turbine measurements. Linear filters are found to be have problems with removing noise while preserving features in the signal. The nonlinear hybrid median filter is found to accurately reproduce the root signal from noisy data. Simulated faulty data and fault-free gas path measurement data are passed through median filters and health residuals for the data set are created. The health residual is a scalar norm of the gas path measurement deltas and is used to partition the faulty engine from the healthy engine using fuzzy sets. The fuzzy detection system is developed and tested with noisy data and with filtered data. It is found from tests with simulated fault-free and faulty data that fuzzy trend shift detection based on filtered data is very accurate with no false alarms and negligible missed alarms.

Learning type of median and mean hybrid filters and a synthesis of its learning signal

Learning type of median and mean hybrid filters and a synthesis of its learning signal

Learning type of median and mean hybrid filters and a synthesis of its learning signal

Learning type of median and mean hybrid filters and a synthesis of its learning signal

High-resolution digital resampling using vector rational filters

Rational filters are extended to multichannel signal process- ing and applied to image interpolation. Two commonly used decimation schemes are considered: a rectangular grid and a quincunx grid. For each decimation lattice, we propose a number of adaptive resampling algorithms based on the vector rational filter (VRF). These algorithms exhibit desirable properties such as edge and detail preservation and accurate chromaticity estimation. In these approaches, color image pix- els are considered as three-component vectors in the color space. Therefore, the inherent correlation that exists between the different color components is not ignored. This leads to better image quality compared to that obtained by componentwise or marginal processing. Extensive simulations show that multichannel image processing with the proposed algorithms (VRFL) and (VRFd) based on l p-norm and directional pro- cessing, respectively; significantly outperform linear and some nonlinear techniques, e.g., vector FIR median hybrid filters (VFMH). Some images interpolated using VRFL and VRFd are presented for qualitative compari- son. These images are free from blockiness and jaggedness, confirming the quantitative results. © 1999 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(99)00105-1)

Class of multichannel image processing filters

A new class of multichannel image processing filters called vector median rational hybrid filters (VMRHFs) for multispectral image processing is introduced and applied to the colour image filtering problem. These filters are based on rational functions. The rational function structure is attractive in filtering since it is a universal approximator and a good extrapolator. The VMRHF is a two stage filter, which exploits the features of the vector median filter and those of the vector rational operator. These filters exhibit desirable properties, such as edge and detail preservation and accurate chromaticity estimation.

Image interpolation based on median-type filters

Image interpolation based on median-type filters is investigated. We study this problem for two decimation schemes that are most commonly used in practice: (1) quincunx lattice and (2) rectangular lattice. For each decimation lattice, we propose a number of interpolation structures that are based on some median or finite impulse response (FIR)-median hybrid filters. Extensive simulations show that the proposed median-based interpolators outperform significantly as compared with linear schemes. To further verify the effectiveness of these medianbased interpolators, we compare them with multilayer neural networks designed through a training process, and still show a preference for the median-based interpolators. As an application, they are used in an interpolative Joint Photographic Experts Group (JPEG) coding scheme to suppress blocking artifacts at very low bit rates.

Edge-preserving smoothing using median mean neural hybrid filters

A novel technique for edge-preserving smoothing is developed by using median mean neural hybrid filters. This filter structure is represented by the cascade connection of a median filter, a mean filter, and a neural network. This filter can adapt itself to the various noise environment through the learning of a training image. In the case where a priori data such a training image is unavailable, this filter can be efficiently applied to edge-preserving smoothing for the images degraded by the Gaussian and impulsive noises. Moreover, the structure of the proposed filter is very simple. Finally, the simulation examples show the effectiveness of the proposed filters.

Median and neural network hybrid (MNNH) filters

Median and neural network hybrid (MNNH) filters

Median and neural network hybrid (MNNH) filters

When a nonstationary signal containing sudden changes, such as an image signal, is degraded by an additive noise, a powerful means to recover the signal is to use a nonlinear filter. This paper uses a layered neural network, and proposes a new method to construct a nonlinear filter for restoring a signal corrupted by mixed noise (i.e., mixed noise composed of Gaussian noise and impulse noise). As the first step, a prototype filter is proposed, which is a combination of a median filter and a linear (averaging) filter. Then, it is shown that the prototype filter can be represented by a network structure. By interpreting the network representation by a layered neural network, the idea is extended to the median neural network hybrid (MNNH) filter. The MNNH filter can be trained by the back-propagation algorithm. The prototype filter already has a high mixed noise elimination performance, but the MNNH filter can further improve the performance by reflecting information on the signal to be processed (the original image and the additive noise) when such information is given. Lastly, the usefulness of the MNNH filter is demonstrated through various application examples. © 1998 Scripta Technica. Electron Comm Jpn Pt 3, 81(6): 52–60, 1998

Effects of Digital Filtering on the Parameters of Sinusoidal Tracking Eye Movements

In the present study linear nonrecursive (FIR) filters designed with the Hamming window and hybrid median filters were examined in connection with computer analysis of sinusoidal tracking eye movements. It is important that digital filters which do not distort sensitive properties of eye movement signals are employed in order to prevent changes caused by too strict filtering in average parameter values of eye movement signals, indicators of some otoneurological disorders. It turned out that within reasonable limits these filters are valid in filtering of sinusoidal tracking eye movement signals.

Feature extraction for real-time expert systems

Feature extraction is a widely used technique in the field of image processing. This paper shows that 2D nonlinear feature extraction is also useful in controllers that are based on artificial intelligence techniques. It can, for example, be used to limit the knowledge explosion problem encountered when online sensors interact with expert systems. The feature extraction scheme suggested uses nonlinear hybrid median filters, is noise tolerant, non-model-dependent, preserves discontinuities, and is capable of a 90% reduction in information representation with negligible signal feature loss.

Weighted FMH filters

In this paper, we introduce new weighted FIR median hybrid (FMH) filters consisting of combinations of averaging and ramp predicting FIR substructures, the input signal and the weighted median operation. We analyze two weighted FMH filter structures in detail. Due to the selection of subfilters the center weighted FMH (CWFMH) filter structure has low-pass characteristics and it preserves sinusoidal signals. It is shown that sinusoidal signals are root signals of the filter. An upper frequency bound for the preservation of sinusoids is derived. The subfilter weighted FMH (SWFMH) filter has the property of preserving edges while filtering high-frequency stationary signals. The characteristics of both filters are analyzed for sinusoidal and pulse shaped input signals. An efficient implementation structure for the weighted FMH filters with complexity independent of the filter length is shown. As examples, removal of artifacts and noise from the electroencephalogram (EEG) and electrooculogram (EOG) signals, respectively, is studied.

An IIR median hybrid filter

A new class of nonlinear filters, the multidirectional infinite impulse response median hybrid (MIMH) filters, is presented and analyzed. The input signal is processed twice using a linear shift-invariant (LSI) infinite impulse response (IIR) filtering module: once with normal causality and a second time with inverted causality. The final output of the MIMH filter is the median of the two-directional outputs and the original input signal. Thus, the MIMH filter is a concatenation of linear filtering (a LSI IIR filtering module) and nonlinear filtering (a median filtering module). Because of this unique scheme, the MIMH filter possesses many desirable properties which are both proven and analyzed (including impulse removal, step preservation, and noise suppression). A comparison to other medium-type filters is also provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

LMS order statistic filters adaptation by backpropagation

Abstract A novel class of nonlinear adaptive filters based on order statistics is presented. An LMS algorithm for their adaptation is proposed. This algorithm is essentially a backpropagation algorithm for the adaptation of coefficients that are used before data sorting. The nonlinear filters that can become adaptive by the techniques presented in this paper are the median hybrid filter, the general nonlinear filter structure, the L -filters and the Ll -filters.