May 12, 2022Computational Intelligence and NeuroscienceOpen Access

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

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Why the study?

Machine learning classification of carotid ultrasound to identify plaque presence and intima-media thickness requires features extracted from images.

Structured PICO

Population

361 carotid artery ultrasound images

Intervention

Feature extraction (65 features) and PCA-based feature selection (22 most significant features) with Naive Bayes and DLVQ machine learning classifications

Outcome

Classification accuracy for identifying plaque presence and intima-media thickness (IMT)surrogate

PCA-based feature selection of 22 significant features from carotid ultrasound images can assist in machine learning classification of plaque presence and IMT.

Implication

May support ML classifiers for carotid plaque and IMT on ultrasound; leaves open external validation before any practice change.

Abstract

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.