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Updated: Sep 22, 2025

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.

S Latha¹, P Muthu², Samiappan Dhanalakshmi¹

¹Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.

Computational Intelligence and Neuroscience

|May 23, 2022

Summary

This study extracts 65 features from carotid artery ultrasound images to detect atherosclerosis. Machine learning, using 22 selected features, improves plaque classification accuracy for stroke risk assessment.

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Area of Science:

Medical Imaging
Machine Learning
Cardiovascular Disease

Background:

Carotid artery plaque is a primary cause of stroke and atherosclerosis.
Ultrasound imaging is crucial for early detection of disease progression.
Machine learning requires robust feature extraction for accurate classification of ultrasound images.

Purpose of the Study:

To extract and select significant features from carotid artery ultrasound images.
To improve the accuracy of plaque detection and intima-media thickness (IMT) classification.
To evaluate machine learning algorithms for carotid artery disease assessment.

Main Methods:

Extraction of 65 diverse features (shape, texture, histogram, correlogram, morphology) from 361 ultrasound images.
Application of Principal Component Analysis (PCA) for feature selection, identifying 22 most significant features.
Implementation of Naive Bayes and Dynamic Learning Vector Quantization (DLVQ) for classification.

Main Results:

Successful extraction and selection of 22 key features from carotid artery ultrasound images.
Demonstrated potential for improved classification accuracy using selected features.
Analysis of Naive Bayes and DLVQ algorithm performance on the dataset.

Conclusions:

Feature extraction and selection are vital for enhancing machine learning-based carotid artery disease classification.
The selected 22 features show promise for improving diagnostic accuracy in ultrasound imaging.
This approach aids in early stroke risk assessment through better plaque identification.