Theoretical aspects of vertically invariant gray-level morphological operators and their application on adaptive signal and image filtering

Chen, Chu-Song; Wu, Ja‐Ling; Hung, Yung‐Jr

doi:10.1109/78.752602

Cited by 37 publications

(3 citation statements)

References 25 publications

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“…The morphological transform is very widespread in the domain of signal processing and image processing because of its robustness and its simple and fast calculation [25,26].…”

Section: Theory Of Mathematical Morphology 21 the Basic Principlementioning

confidence: 99%

Mathematical Morphology and the Heart Signals

Ahmed¹

2022

Biomedical Engineering

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Nowadays, signal processing is integrated into most systems for analyzing and interpreting ECG and PCG signals. Its objectives are multiple and mainly include compensating for the addition of artifacts to the signals of interest, and extracting information that is not visible by direct visual analysis. Considering that useful clinical information is found in the characteristic waves of the ECG, in particular, the P wave, the QRS complex, the T wave and the heart sounds of the PCG signal. These signals provide important indicators for the diagnosis of heart disease because they reflect physiological processes. These are non-stationary signals that are very sensitive to noise, hence the need to have optimal conditions to record them. It is necessary to use appropriate signal processing tools for noise suppression and wave detection of the ECG signal. Our method uses Morphological filtering, multi-scale morphological and the other by top-hat transform, which are based on mathematical morphology. The latter is based on mathematical operators called opening and closing morphology operators. These methods are also tested, with the aim of removing the noise and detection of the waves of the ECG signal and of the pathological sounds of the PCG signal.

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“…The morphological transform is very widespread in the domain of signal processing and image processing because of its robustness and its simple and fast calculation [25,26].…”

Section: Theory Of Mathematical Morphology 21 the Basic Principlementioning

confidence: 99%

Mathematical Morphology and the Heart Signals

Ahmed¹

2022

Biomedical Engineering

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“…In morphological filtering [8][9][10][11], each signal is viewed as a set, and its geometrical features are modified by morphologically convolving the signal with a structuring element (SE), which is another set of simple shape and size. By varying the structuring element we can extract different types of information from the signal.…”

Section: B Heartbeat Classifiermentioning

confidence: 99%

Hybrid wavelet-mathematical morphology feature extraction for heartbeat classification

Tadejko

Rakowski

2007

EUROCON 2007 - The International Conference on "Computer as a Tool"

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The paper presents the classification performance of an automatic classifier of the electrocardiogram (ECG) for the detection abnormal beats with new concept of feature extraction stage. Feature sets were based on ECG morphology and RR-intervals. This paper compares two strategies for classification of annotated QRS complexes: based on original ECG morphology features and proposed new approach -based on preprocessed ECG morphology features. The mathematical morphology filtering and wavelet transform is used for the preprocessing of ECG signal. Within this framework, the problem of choosing an appropriate wavelet in signal preprocessing was studied. Configuration adopted a Kohonen self-organizing maps (SOM) and Support Vector Machine (SOM) for analysis of signal features and clustering. In this study, a classifiers was developed with SOM/LVQ and SVM algorithms using the data from the records recommended by ANSI/AAMI EC57 standard. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. Using this method the results of identify beats either as normal or arrhythmias was improved.

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“…A grayscale image can be considered as a three-dimensional set where the first two elements are the x and y coordinates of a pixel and the third element is the gray-scale value. The key issue is to use the infima/suprema (minima and maxima in discrete cases) to define gray-scale morphological operators (Chu-Song et al 1999;Heijmans 1999). The structuring elements of the gray-scale morphological operations could have the same domains as those in binary morphology.…”

Section: Gray Morphological Image Processing Operationsmentioning

confidence: 99%