Stratification of risk of atherosclerotic plaque using Hu’s moment invariants of segmented ultrasonic images
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Smitha Balakrishnan
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Paul K. Joseph
Abstract
Myocardial infarction is one of the major life-threatening diseases. The cause is atherosclerosis i.e. the occlusion of the coronary artery by deposition of plaque on its walls. The severity of plaque deposition in the artery depends on the characteristics of the plaque. Hence, the classification of the type of plaque is crucial for assessing the risk of atherosclerosis and predicting the chances of myocardial infarction. This paper proposes prediction of atherosclerotic risk by non-invasive ultrasound image segmentation and textural feature extraction. The intima-media complex is segmented using a snakes-based segmentation algorithm on the arterial wall in the ultrasound images. Then, the plaque is extracted from the segmented intima-media complex. The features of the plaque are obtained by computing Hu’s moment invariants. Visual pattern recognition independent of position, size, orientation and parallel projection could be done using these moment invariants. For the classification of the features of the plaque, an SVM classifier is used. The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. Tenfold cross-validation protocol is used for training and testing the classifier. An accuracy of 97.9% is obtained with only two features. This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists. The segmentation step introduced in the preprocessing stage improved the feature extraction technique. An improvement in performance is achieved with much less number of features.
Acknowledgment
We would like to acknowledge the help provided by Cyprus Institute of Neurology and Genetics, in Nicosia, Cyprus, for providing the image database.
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Research funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Conflict of interest statement: The authors declare that there is no conflict of interests regarding the publication of this paper.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: Not applicable.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research Articles
- Biomagnetic signals recorded during transcranial magnetic stimulation (TMS)-evoked peripheral muscular activity
- Analysis of pilots’ EEG map in take-off and landing tasks
- A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal
- A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs
- Stratification of risk of atherosclerotic plaque using Hu’s moment invariants of segmented ultrasonic images
- A new method for successful indirect bonding in relation to bond strength
- Automatic landmark identification for surgical 3d-navigation – A proposed method for marker-free dental surgical navigation systems
- Mechanical response of different frameworks for maxillary all-on-four implant-supported fixed dental prosthesis: 3D finite element analysis
