Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Ultrasound Images of the Intima and Media Layers of the Carotid Artery

Abstract

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Clinically, there is strong interest in identifying how the composition and texture of the media layer (ML) can be associated with the risk of stroke. In this study, we use 2-D amplitude-modulation frequency-modulation (AM-FM) analysis of the intima-media complex (IMC), the ML, and intima layer (IL) of the CCA to detect texture changes as a function of age and sex. The study was performed on 100 ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. To investigate texture variations associated with age, we separated them into three age groups: 1) patients younger than 50; 2) patients aged between 50 and 60 years old; and 3) patients over 60 years old. We also separated the patients by sex. The IMC, ML, and IL were segmented manually by a neurovascular expert and also by a snake-based segmentation system. To reject strong edge artifacts, we prefilter with an AM-FM filterbank that is centered along the horizontal frequency axis (parallel to the long axis of the IMC, ML, and IL), while removing the low-pass filter estimates and frequency bands with large, vertical frequency components. To investigate significant texture changes, we extract the instantaneous amplitude (IA) and the magnitude of the instantaneous frequency (IF) over each layer component, for low-, medium-, and high-frequency AM-FM components. We detected significant texture differences between the higher risk age group of >60 years versus the lower risk age group of <50 and the 50-60 group. In particular, between the <50 and >60 groups, we found significant differences in the medium-scale IA extracted from the IMC. Between the >60 and the 50-60 groups, we found significant texture changes in the low-scale IA and high-scale IF magnitude extracted from the IMC, and the low-scale IA extracted from the IL. Also, we noted that the IA for the ML showed significant differences between males and females for all age groups. The AM--FM features provide complimentary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. These findings provide evidence that AM--FM texture features can be associated with the progression of cardiovascular risk for disease and the risk of stroke with age. However, a larger scale study is needed to establish the application in clinical practice.