Carotid artery ultrasound image analysis: A review of the literature

Abstract

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Evidence of woven bone formation in carotid artery plaques

“…Polarised light microscopy identi ied two different types of plaques, a soft one with a transparent structure and without the presence of bone tissue, called soft plaques, and hard plaques with woven bone structures. These indings are probably identical with echolucent (predominant lipid core) und echo-opaque plaques (predominantly ibrous tissue/ calcium) as identi ied by sonography and magnetic resonance tomography [27][28][29][30][31]. The major inding of our study is the presence of woven bone tissue in hard carotid plaques.…”

Fully Automated Integrated Segmentation of Carotid Artery Ultrasound Images Using DBSCAN and Affinity Propagation

“…With a threshold considered from the gray histogram, the remaining points are marked as reference to the edge points. Based on the adaptive threshold, the number of data points can be controlled [31,40,41]. A mixture density distribution function for l different density types is given by where w i is the weight of the ith density kind, m is the distance order and j is the jth intensity form.…”

Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning

“…Doppler US helps assess stroke risk, to identify and evaluate narrowing or blockages in the arteries in the neck and head. This procedure is useful for evaluating people who have had a stroke and who have risk factors for atherosclerosis but no symptoms [5]. Some studies have been proposed to localize and segment the CCA in transverse mode images.…”