Adaptive Mathematical Morphology Research Articles

A non-contact current traveling wave fault location method for multi-terminal overhead power lines based on adaptive mathematical morphology

A non-contact current traveling wave fault location method for multi-terminal overhead power lines based on adaptive mathematical morphology

Combining Adaptive Mathematical Morphology and Visual Reconstruction Theory for Animation Creation

Abstract This paper first discusses the use of traditional elements in animation creation and analyzes the linear narrative and artistic expression involved in its creation. Secondly, in order to carry out the image design of animation creation, the color adaptive mathematical morphology is used to carry out the image edge calculation, and the ICP algorithm in the visual reconstruction theory is introduced to carry out the point cloud alignment and fusion. Finally, the effectiveness of this paper’s method is evaluated by comparing algorithms and examples. According to the findings, the loss rate of expansion and corrosion details using the color adaptive mathematical morphology operator is 0.05% and 5.69%, respectively. The overall evaluation of the visual reconstruction presentation is 94.93% at grade C or above. This shows that using the method of this paper can accurately realize the image design of animation creation, enhance the audience’s perception of animation with a clearer view, and then promote the prosperity of the animation industry.

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

Background and objectiveThe automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times.MethodsFirst, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager–Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals.ResultsThe best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%).ConclusionThe proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network.

In order to solve the problem of low efficiency and accuracy of injury image recognition for sports athletes in high-intensity injury treatment, this paper proposes an injury recognition mode based on the deep neural network. In this paper, the image of sports injury is converted to gray level, and the contour of the injury part in the image is extracted according to the combination of adaptive thresholding and mathematical morphology. In this model, the seed points are selected, the active contour is used to approximate the initial contour, and the curve fitting method is used to fit the obtained discrete points to obtain the final damaged contour. The digital matrix is constructed by using the extracted number of pixels at the damaged position and relevant information. The images are arranged into feature vectors with a length of 64 according to the mode of column concatenation. The overall mean vector of the image is calculated. The calculation results, training samples, and image samples to be recognized are substituted into the Euclidean distance to obtain the preliminary recognition results of the damaged position of the image of sports injury. Then, the image segmentation is realized by clustering. The clustering segmentation results are used to color describe the pixel categories of the original image, calculate the relative damage proportion area in the sports injury image, and identify the damage parts of the high-intensity sports injury image. The experimental results show that the recognition rate of the neural network is 80%-100%, and the recognition time of this method is 0-0.6/s. The above method can improve the accuracy of the recognition of the damaged part of the sports injury image and shorten the recognition time and has certain feasibility in determining the sports injury part.

A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

SIMULATION OF RECOGNITION METHOD OF DAMAGED PARTS OF HIGH-INTENSITY SPORTS INJURY IMAGES

ABSTRACT Introduction: When athletes are performing sports training, many movements are of high intensity, and that training is repetitive, resulting in wear and tear on some injured parts. Objective: Sports athletes can damage parts of the body in high - intensity exercise. During the processing, it is necessary to identify and analyze the damaged parts in the image. However, the current relevant methods have low accuracy and different problems of efficiency and quality. Methods: In this paper, a Fish Swarm Algorithm is proposed to identify high-intensity motion damage images. According to the combination of adaptive threshold and mathematical morphology, the contour of the damaged part of the image is extracted. Results: The above-mentioned method can improve the accuracy of identifying damaged parts of sports injury images, shorten the recognition time, and has certain feasibility in determining sports injury parts. Conclusions: This method can be widely used in high-intensity sports injuries. Level of evidence II; Therapeuticstudies - investigation of treatment results.

Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions

Abstract This paper proposes a way of better approximating continuous, two-dimensional morphology in the discrete domain, by allowing for irregularly sampled input and output signals. We generalize previous work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentally we show improved results over regular, discrete morphology with respect to the approximation of continuous morphology. It is also worth noting that the number of output samples can often be reduced without sacrificing the quality of the approximation, since the morphological operators usually generate output signals with many plateaus, which, intuitively do not need a large number of samples to be correctly represented. Finally, the paper presents some results showing adaptive morphology on irregularly sampled signals.

Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting from the low threshold is taken as the marker image, and that from the high threshold is taken as mask image, and mathematical morphology reconstruction is performed on the two images, with the resulting image being the preliminary result of the wheel defect segmentation. Finally, with area and diameter parameters as the conditions, the preliminary segmentation result is analyzed, and the defect regions satisfying the conditions are taken as the ultimate result of the whole solution. Experiments proved the feasibility of the above solution, which is found capable of extracting different types of wheel defects satisfactorily.

A New Kind of Image Edge Detection Based on The Theory of The Adaptive Lifting Wavelet and Morphology

This article describes how in traditional edge detection it is prone to defects such as fuzzy positioning, and noise influence. This article proposes a type of edge detection algorithm which combines lifting wavelet transform and adaptive mathematical morphology, which makes a lifting wavelet to analyze the wood cell image. Then, the high-frequency part is detected by using the algorithm fusing the wavelet packet and the rapid-combining multi-scale wavelet, which controls noise effectively; while for the low frequency part is detected with modified adaptive mathematical morphology, to locate the exact details. The final result will processes the edge of the image using “algebra” algorithm fusion. The example for a wood cell image which illustrates the algorithm is to detect the cell boundary relatively clearly, and effectively suppress the noise.

A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures.

Extraction of strong beadlike reflections for a carbonate-karst reservoir using a tensor-based adaptive mathematical morphology

Extraction of strong beadlike reflections for a carbonate-karst reservoir using a tensor-based adaptive mathematical morphology

Impulsive Noise Removal by Adaptive Mathematical Morphology

Impulsive Noise Removal by Adaptive Mathematical Morphology

False arrhythmia alarms reduction in the intensive care unit: a multimodal approach

The purpose of this study was to develop algorithms to lower the incidence of false arrhythmia alarms in the ICU using information from independent sources, namely electrocardiogram (ECG), arterial blood pressure (ABP) and photoplethysmogram (PPG). Our approach relies on robust adaptive signal processing techniques in order to extract accurate heart rate (HR) values from the different waveforms. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we developed a supplementary measure based on the spectral purity of the ECGs to determine whether a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on HR and spectral purity values. The proposed method was evaluated on the PhysioNet/CinC Challenge 2015 database, which is composed of 1250 life-threatening alarm recordings, each categorized into either bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. This resulted in overall true positive rates of 95%/99% and overall true negative rates of 76%/80% on the real-time and retrospective subsets of the test dataset, respectively.

Automatic Reference Color Selection for Adaptive Mathematical Morphology and Application in Image Segmentation.

This paper proposes a novel automatic reference color selection (ARCS) scheme for the adaptive mathematical morphology (MM) method, and is specifically designed for color image segmentation applications. Because of the main advantages of being intuitive and simple, in the past decade, it has contributed to the growing popularity of binary and gray-scale MM processing. However, the MM process typically neglects the details of reference color determination. Applying other ordering methods, which select only black as the reference color for sorting pixels, result in the problem in which the scope of the distance measurement is not optimal. The proposed ARCS scheme is used for determining the ideal reference color for MM and for color image segmentation application. In addition, we use both 1D histogram-based modeling scheme binning from 3D color spaces, such as red-green-blue and hue-saturation-intensity, and 2D color models, such as (H, S), (Cb, Cr), and (I, By). According to the results of the quartile analysis, the threshold determination reacts with less sensitivity to the context variations of the images tested. The experiments focused on color-based image segmentation using the proposed ARCS scheme for color MM processing through a bottom-up scenario. To evaluate the system, four quantitative indices were utilized for an ARCS comparison using advanced segmentation methods in the experiments. The cross validation with different system parameters and a comparison of the morphological gradient operation with different color models are also presented.

Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology

QRS complex detection in the electrocardiogram (ECG) has been extensively investigated over the last two decades. Still, some issues remain pending due to the diversity of QRS complex shapes and various perturbations, notably baseline drift. This is especially true for ECG signals acquired using wearable devices. Our study aims at extracting QRS complexes and their fiducial points using Mathematical Morphology (MM) with an adaptive structuring element, on a beat-to-beat basis. The structuring element is updated based on the characteristics of the previously detected QRS complexes for a more robust and precise detection. The MIT-BIH arrhythmia and Physionet QT databases were respectively used for assessing the detection performance of R-waves and other fiducial points. Furthermore, the proposed method was evaluated on a wearable-device dataset of ECGs during vigorous exercises. Results show comparable or better performance than the state-of-the-art with a 99.87% sensitivity and 0.22% detection error rate for the MIT-BIH arrhythmia database. Efficient extraction of QRS fiducial points was achieved against the Physionet QT database. On the wearable-device dataset, an improvement of more than 10% in QRS complex detection rate compared to classic approaches was obtained.

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.

Automatic cell nuclei segmentation and classification of breast cancer histopathology images

Breast cancer is the leading type of malignant tumor observed in women and the effective treatment depends on its early diagnosis. Diagnosis from histopathological images remains the "gold standard" for breast cancer. The complexity of breast cell histopathology (BCH) images makes reliable segmentation and classification hard. In this paper, an automatic quantitative image analysis technique of BCH images is proposed. For the nuclei segmentation, top-bottom hat transform is applied to enhance image quality. Wavelet decomposition and multi-scale region-growing (WDMR) are combined to obtain regions of interest (ROIs) thereby realizing precise location. A double-strategy splitting model (DSSM) containing adaptive mathematical morphology and Curvature Scale Space (CSS) corner detection method is applied to split overlapped cells for better accuracy and robustness. For the classification of cell nuclei, 4 shape-based features and 138 textural features based on color spaces are extracted. Optimal feature set is obtained by support vector machine (SVM) with chain-like agent genetic algorithm (CAGA). The proposed method was tested on 68 BCH images containing more than 3600 cells. Experimental results show that the mean segmentation sensitivity was 91.53% (±4.05%) and specificity was 91.64% (±4.07%). The classification performance of normal and malignant cell images can achieve 96.19% (±0.31%) for accuracy, 99.05% (±0.27%) for sensitivity and 93.33% (±0.81%) for specificity.

Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists’ visual predictions.

Mammographic image analysis based on adaptive morphological fuzzy logic CAD system

The proposed method is the cancer detection in mammogram images based on adaptive mathematical morphology and fuzzy logic approach. The mathematical morphology is a power tool based on the set theory meant to extract various features from colour and grey level images. These operators are adaptive with respective to space or intensity along with the non-linear filters for giving the accurate features of image. Fuzzy logic offers an efficient mathematically logical background to process and generate images with countless features. The proposed methodology highlights the usage of adaptive mathematical morphological operators and fuzzy logic on mammograms followed by converting the suspected area into colour space to identify the intensity level and shape of the suspicious area. Experimental results show the advanced characteristics of cancer images, the proposed method is demonstrated with various example images and evaluated metrics are tabulated.

Adaptive mathematical morphology – A survey of the field

We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.