Abstract

The geometry of the imaged transverse cross-section of carotid arteries in in-vivo B-mode ultrasound images are most times irregular, unsymmetrical, full of speckles and usually non-uniform. We had earlier developed a technique of cardinal point symmetry landmark distribution model (CPS-LDM) to completely characterize the Region of Interest (ROI) of the geometric shape of thick-walled simulated B-mode ultrasound images of carotid artery imaged in the transverse plane, but this was based on the symmetric property of the image. In this paper, this developed technique was applied to completely characterize the region of interest of the geometric shape of in-vivo B-mode ultrasound images of non-uniform carotid artery imaged in the transverse plane. In order to adapt the CPS-LD Model to the in-vivo carotid artery images, the single VS-VS vertical symmetry line common to the four ROIs of the symmetric image is replaced with each ROI having its own VS-VS vertical symmetry line. This adjustment enables the in-vivo carotid artery images possess symmetric properties, hence, ensuring that all mathematical operations of the CPS-LD Model are conveniently applied to them. This adaptability was observed to work well in segmenting the in-vivo carotid artery images. This paper shows the adaptive ability of the developed CPS-LD Model to successfully annotate and segment in-vivo B-mode ultrasound images of carotid arteries in the transverse cross-sectional plane either they are symmetrical or unsymmetrical.

Highlights

  • Oftentimes, strokes are caused when there is a blockage in an artery leading to the blood supply to the brain being restricted or cut off

  • The carotid artery is divided into three vessels: the common carotid artery (CCA), the external carotid artery (ECA) and the internal carotid artery (ICA)

  • The carotid artery imaged in the transverse plane in conjunction with that made in the longitudinal plane help to determine the intima-media thickness (IMT) of the arterial wall

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Summary

INTRODUCTION1

Oftentimes, strokes are caused when there is a blockage in an artery leading to the blood supply to the brain being restricted or cut off. We proceeded to apply this developed model on the geometric shape of transverse cross-section of thin-walled phantom carotid arteries in B-mode ultrasound images [13]. A reliable detection of sectional area of an artery during a time in the B-mode ultrasound video sequence frames by automatic monitoring of artery border tissue movement using the Lucas-Canade optical flow technique was developed by [8]. [11] developed a method which employed a Viola-Jones detector for efficient detection of transverse sections of the carotid artery Their algorithm was trained on a set of labelled images using AdaBoost algorithm, Haar-like features, and the Matthews coefficient.

ADAPTING THE CPS-LDM MODEL TO THE IN-VIVO CAROTID ARTERY IMAGE
Example 2
Findings
CONCLUSION
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