Stroke Risk Assessment Through Sparse AM-FM Decompositions of Carotid Plaque Ultrasound Images

Abstract

The objective of this work was to investigate a new sparse multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on multiple Gabor filterbanks representations where component selection was carried out using the elastic net regularization equation. The AM-FM histogram features sets of instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency were computed from carotid plaque ultrasound images to assess the risk of stroke. A total of 100 carotid plaque ultrasound images (50 asymptomatic and 50 symptomatic) were analyzed following manual segmentation by an expert. Classification modelling was carried out using the Support Vectors Machine to classify asymptomatic versus symptomatic plaques. An overall classification accuracy of 74% was achieved, demonstrating that the new sparse multiscale AM-FM analysis provided robust features. These findings are comparable with classification models trained with traditional AM-FM feature sets as well as classical texture feature sets. Moreover, the proposed analysis provides new sparse image representations that allow us to reduce the number of AM-FM components needed to explain the local spatial-frequency content and can further facilitate the desired explanatory interpretation in stroke risk assessment.