Multi-scale Mathematical Morphology Research Articles

An adaptive feature extraction technique for ship-radiated noise based on weighted multiscale mathematical morphological filtering

Aiming at the problem of feature extraction of ship-radiated noise in complex marine environment, an adaptive weighted multiscale mathematical morphological filtering method is proposed. The weight coefficients of the structure elements of different scales in multiscale morphological filtering are determined adaptively by an improved particle swarm optimization algorithm, and the Teager energy kurtosis is used as the evaluation index of the filtered optimal signal to provide optimized weights for each scale. Finally, the optimized multiscale mathematical morphology filter with selective adaptive weights is bound and applied to the feature extraction of ship-radiated noise. The analysis results of simulated signal and measured ship-radiated noise show that this method has strong noise suppression and feature extraction ability, and the calculation is simple and fast, which provides an effective method for ship feature extraction.

Traveling wave velocity independent fault location scheme with adaptive mathematical filters for distribution network

In this paper, a novel traveling wave fault location scheme suitable for multi-branch distribution network is proposed. First, to overcome the negative impact of noise signals, a method based on multiscale mathematical morphology is designed to extract the arrival time of fault-generated traveling waves, with the structure elements adaptively adjusted according to the characteristic of input signals. Then, two proper criteria derived from multi-end traveling wave method are used to identify the faulty line section and filter the valid time recorded by traveling wave detectors, respectively. Finally, a velocity-independent formula is presented to achieve the accurate fault location. Extensive simulation tests are carried out to evaluate the feasibility of the proposed scheme, and the simulation results verified that it is independent of the fault location, inception angle, impedance and noise interference.

Recognition of Hydrophobicity Class of Polymeric Insulators Employing Residual Morphological Neural Network and Granulometry-Based Image Analysis

Hydrophobicity is an important property of polymeric insulators that allow formation of discrete water droplets on the insulator surface. However, with aging or under extreme humidification, the insulator surface loses its hydrophobic property, resulting in continuous water channel formation, which may lead to dry band arcing and subsequently flashover, thereby affecting the long-term performance of the insulator. Therefore, accurate recognition of hydrophobic class (HC) is important as it provides information regarding the surface condition of the insulator. Considering the above-said facts, this article investigates the feasibility of employing residual morphological neural network (Res-Morph-NN) to accurately recognize different HCs of polymeric insulators. In addition, for better understanding of the distribution of water droplets on the insulator surface, a novel approach based on multiscale mathematical morphology, i.e., granulometry, is used in this article. Our investigations revealed significant alterations in the granulometric patterns with the change in HC of polymeric insulators. The efficacy of the proposed framework employing Res-Morph-NN has been validated on HC images obtained from the experiment as well as on large set of images obtained from an online database, which returned satisfactory results. The proposed method can be efficiently used to discriminate different hydrophobicity classes, which can be implemented for monitoring insulator surface condition in real time.

Classification for transient overvoltages in offshore wind farms based on multi-scale mathematical morphology

At present, the transient overvoltages in o shore wind farms caused by faults or frequent operations of electrical equipment are particularly severe. The quick and accurate classi cation of transient overvoltages is important for protecting electrical equipment from damage. In order to classify the internal transient overvoltages in o shore wind farms, this research rstly proposes a feature extraction method based on mathematical morphology, which proposes a new morphological structure element and utilizes a multi-scale mathematical morphology to extract the high/low-frequency components of transient overvoltages. Then a high-frequency feature and a high/low-frequency energy ratio feature are constructed as identi cation features. Finally, based on the constructed features, a support vector machine is employed to identify di erent types of internal transient overvoltages. Extensive simulations and experiments are performed to verify the superiority of the proposed feature extraction method based on mathematical morphology, which is also compared with the widely used conventional wavelet algorithms. Results show the proposed mathematical morphology feature extraction method is capable of classifying and discriminating among various types of internal transient overvoltages with a simple procedure and an improved accuracy. The proposed method provides a valuable reference for the protection and insulation coordination of electrical equipment in o shore wind farm substations.

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Research on current harmonic suppression of PMSM based on mathematical morphology

Due to the existence of power converter dead time, flux harmonics and other nonlinear factors, the motor phase current contains a large number of high-order harmonics, so the electromagnetic torque generated by the interaction of current and flux also contains various orders of harmonics. In order to eliminate current harmonics, mathematical morphology filter is proposed to suppress current harmonics. However, the single-scale morphological filtering has shortcomings in preserving information details. In order to make up for the deficiency of the traditional single-scale morphological filtering in preserving information details, a weighted multi-scale mathematical morphology method is proposed to suppress the current harmonics.

Novel hybrid signal processing approach based on empirical mode decomposition and multiscale mathematical morphology for islanding detection in distributed generation system

A novel islanding detection technique by hybridising empirical mode decomposition (EMD) and multi-scale mathematical morphology (MMM) is proposed to detect the islanding condition in a distributed generation system to ensure personnel and equipment safety. The proposed method first uses EMD to efficiently separate the collected raw signal into the number of intrinsic mode functions (IMFs) with different frequency scales and the signal is reconstructed considering important IMFs which carry transient features for further analysis using their correlation coefficients. MMM is used for determining a ratio index named as MMMRI, the threshold value of the proposed MMMRI decides islanding and other power quality (PQ) disturbances. The proposed hybrid method name coined as EMD-MMMRI. The main motivation behind hybridising two signal processing techniques is to reduce detection time and improve accuracy. The efficacy of the method is demonstrated for different PQ disturbances and islanding events simulated on a grid-connected, heavily wind energy penetrated distributed generation system using MATLAB/Simulink environment. The test bench validation of the proposed technique is obtained through TMS 320C6713 Starter Kit (DSK) in digital signal processor platform. The efficacy of proposed work is demonstrated with large number of simulation studies.

Faster Islanding Detection of Microgrid Based on Multiscale Mathematical Morphology

Faster and reliable islanding detection of microgrid is necessary to protect the equipment and maintenance personnel. Voltage and frequency are two important parameters needs to be controlled in microgrid; Voltage and frequency stability of microgrid depends merely on main grid during grid connected mode and depends on individual controllers during islanding mode. This research proposes a time domain technique based on multi-scale mathematical morphology (MMM) for islanding detection. The proposed technique uses multiscale dilation-erosion difference filter (MDEDF) with peak value of the signal. The performance of the proposed technique is validated in IEEE-13bus system with different cases such as mismatch in real power, reactive power, load switching, motor switching; L-G fault .The results validate the accuracy and efficacy of the proposed technique and also compared with recently published work.

Edge detection on terahertz pulse imaging of dehydrated cutaneous malignant melanoma embedded in paraffin

Terahertz pulse imaging of cutaneous malignant melanoma dehydrated by ethanol and embedded in paraffin was carried out across a frequency range of 0.2–1.4 THz. First, the tissue images based on the time-domain electric-field amplitude information were acquired. Then the areas of normal and cancerous tissues were determined using multi-scale, multi-azimuth and multi-structural element mathematical morphology. The physical meaning of the image was analyzed by calculation of the refractive index and absorption coefficient of cutaneous malignant melanoma in different areas. The refractive index of both normal and cancerous tissues showed anomalous dispersion. The refractive index of cancerous tissues tended to vary between 0.2 and 0.7 THz, while that of normal and fat tissues remain almost unchanged. The absorption of cancerous tissues was higher, with a maximum at 0.37 THz. We concluded that both the refractive index and absorption coefficient differ considerably between normal and cancerous tissues, and the areas of normal and abnormal tissues can be identified using THz pulse imaging combined with mathematical morphology. The method for edge detection of terahertz pulse imaging of cutaneous malignant melanoma provides a reference for the safe surgical removal of malignant tumors.

Texture description using multi-scale morphological GLCM

Texture is the collective repetitive pattern that characterizes the surface of real world objects. The main challenge in the texture description is its application specific definition. The present work aims at bringing the definition of textures under a generalized framework and propose some texture descriptors. In order to accomplish this, authors have extensively studied the properties of texture, drawn four observations and used some of them to devise two texture descriptors under the framework of multi-scale mathematical morphology and co-occurrence matrices. Thereafter, the descriptors are used for texture classification and tested on three benchmark datasets. Before applying the descriptors to texture classification, a dependence between number of decomposition levels (scales) and classification percentage is established using hypothesis testing. Once the dependence is established, the corresponding scale and distance parameter is chosen for each dataset. The classification results are compared with a number of existing methods. The efficacy of results prove the supremacy of the proposed methods over the existing ones.

Application of optimized multiscale mathematical morphology for bearing fault diagnosis

In order to suppress noise effectively and extract the impulsive features in the vibration signals of faulty rolling element bearings, an optimized multiscale morphology (OMM) based on conventional multiscale morphology (CMM) and iterative morphology (IM) is presented in this paper. Firstly, the operator used in the IM method must be non-idempotent; therefore, an optimized difference (ODIF) operator has been designed. Furthermore, in the iterative process the current operation is performed on the basis of the previous one. This means that if a larger scale is employed, more fault features are inhibited. Thereby, a unit scale is proposed as the structuring element (SE) scale in IM. According to the above definitions, the IM method is implemented on the results over different scales obtained by CMM. The validity of the proposed method is first evaluated by a simulated signal. Subsequently, aimed at an outer race fault two vibration signals sampled by different accelerometers are analyzed by OMM and CMM, respectively. The same is done for an inner race fault. The results show that the optimized method is effective in diagnosing the two bearing faults. Compared with the CMM method, the OMM method can extract much more fault features under strong noise background.

A Novel Algorithm of Edge Location Based on Omni-directional and Multi-scale MM

In order to suppress image noise effectively and extract the edge clearly and accurately, a novel approach based on omni-directional and multi-scale mathematical morphology for edge detection is proposed in this paper. In order to compare different object geometries a modified Hausdorff distance application is composed. With a new moments rule used, Opening operation and closing operation of large scale structural elements are adopted for suppression of noise, while small scale structural elements are used for edge extraction. Finally, the weighting combination calculation of edge maps acquired under different directions is executed according to different weights. The experimental results show that the improved algorithm has a great promotion in extracting edge information compared with several reference algorithms.

Broadband noise suppression and feature identification of ECG waveforms using mathematical morphology and embedding theorem

The paper presents an adaptive morphological filter developed using multiscale mathematical morphology (MM) to reject broadband noise from ECG signals without affecting the feature waveforms. As a pre-processing procedure, the adaptive morphological filter cleans an ECG signal to prepare it for further analysis. The noiseless ECG signal is embedded within a two-dimensional phase space to form a binary image and the identification of the feature waveforms is carried out based on the information presented by the image. The classification of the feature waveforms is implemented by an adaptive clustering technique according to the geometric information represented by the image in the phase space. Simulation studies on ECG records from the MIT-BIH and BIDMC databases have demonstrated the effectiveness and accuracy of the proposed methods.

A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis

The condition monitoring and fault diagnosis of rolling element bearings play an important role in the safe and reliable operation of rotating machinery. Feature extraction based on vibration signals is an effective means to identify the operating condition of rolling element bearings. Methods based on multi-scale mathematical morphology (MM) have recently been developed to extract features from one-dimensional signals. In this paper, a new double-dot structuring element (SE) is constructed for multi-scale MM. A pattern spectrum, obtained from the multi-scale MM, is used as a feature extraction index. A correlation analysis gives the final identification result by utilizing information over a whole pattern spectrum. Compared with the most commonly used flat SE, the double-dot SE can extract more features of original signals at different scales. Vibration signals, measured from defective bearings with outer race faults, inner race faults and ball faults, are used to evaluate the fault detection ability of the proposed SE and bearing fault diagnosis method. Results show that faults at different levels can be identified, including ball fault; and the location of outer race fault can also be differentiated.

An Improved Technique for Gray Scale Image Enhancement based on Multiscale Morphological Approach

A method for enhancing the contrast of gray scale images using multiscale mathematical morphology is presented in this paper. The conventional theoretical concept of enhancing image has been extended from mathematical morphology to multiscale mathematical morphological approach. Gray scale images sometimes suffer from lack of contrast. So to enhance image efficiently a multiscale top hat transform based algorithm is proposed. In this algorithm multiscale structuring element of same shape and increasing size is used. By using multiscale top hat transform both white and black features at various scales of gray scale image are extracted and these features are added together to find the final enhanced image. For implementing scheme different morphological towers are built. Experiment has been performed on a set of raw gray scale images for testing efficiency both qualitatively and quantitatively and result is also compared with other state of art techniques and gets better result.

Discrimination ability improvement of invariant moment based on multi-scale mathematical morphology

Discrimination ability of invariant moment is crucial for similar object recognition and classification. To improve the discrimination ability of invariant moment for similar object classification, a multi-scale mathematical morphology based algorithm is proposed. Firstly, the multi-scale operations are used to extract the details of image at different scales. Then, the details of multi-scales are combined to form the very different part of the similar objects, and the corresponding invariant moments of the very different parts are calculated. Finally, a measure which is similar to the between-to within-class variance ratio is used to select the efficient invariant moments of the very different parts of several scales. The selected moments of the very different parts of the similar objects will be more efficient than the original invariant moments of original images. Hu moment is adopted as an example to show the efficiency of improvement. And, any type of invariant moment can be used to replace Hu moment in our algorithm to achieve a more efficient invariant moment algorithm. So, our proposed algorithm is very important and can be widely used in different similar object discrimination. The application of improved Hu moment by our algorithm are compared with the original Hu moment and wavelet moment, experimental results show that our algorithm is very efficient.

Image denoising method used in earthquake ruin scene based on multi-scale mathematical morphology

A modified method of image denoising based on multi-scale mathematical morphology is proposed to be used in the earthquake ruin scene. A new morphological arithmetic operator is desiged according to the basic operations in the mathematical morphology, then the multi-scale operation filter is used based on new operator to realize image denoising. Compared with the other methods, this new method can reduce noise of earthquake ruin scene effectively and has a good practicability.

Semiautomatic Method for Segmenting Pedicles in Vertebral Radiographs

The top and bottom of the pedicles are used as landmarks for 3D stereo radiographic reconstruction of vertebrae. At present the landmark identification is manual that can cause observer variability which in turn reduces the accuracy of 3D stereo radiographic reconstruction. A semiautomatic method for segmenting pedicles from biplanar (PA and Lateral) vertebral radiographs is proposed here. Mathematical morphology is proposed for enhancement and Gradient Vector Flow (GVF) snake model is proposed for pedicle segmentation. The conventional theoretical concept of image enhancement has been extended to the regime of multiscale mathematical morphology. Structuring element in this method is multiscale. Bright and dark features at various scales of radiographs are extracted using multiscale tophat transformation. These multiscale features are combined to reconstruct the final modified image. Therefore the contrast of radiograph is enhanced locally.GVF snake model is used for segmenting the pedicles from the enhanced radiograph. A comparison is made between the segmented radiographs with and without the morphological enhancement. Results demonstrated that the distance between contours manually delineated by the user and those segmented by the proposed algorithm is far less than the distance resulted from the traditional GVF snake without morphological enhancement. The morphological enhancement produces better segmentation results even with noisy and low contrast radiographs. The proposed method enables the automatic landmark identification from the segmented radiographs which will remove observer variability that in turn increases the 3D reconstruction accuracy. Hence the proposed method might be a useful preprocessing tool for 3D stereo radiographic reconstruction.

Lung Nodule Segmentation Using Mathematical Morphology

Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.