Abstract

Atherosclerosis is responsible for a large proportion of cardiovascular diseases (CVD), which are the leading cause of death in the world. The atherosclerotic process, mainly affecting the medium- and large-size arteries, is a degenerative condition that causes thickening and the reduction of elasticity in the blood vessels. The Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) is a reliable early indicator of atherosclerosis. Usually, it is manually measured by marking pairs of points on a B-mode ultrasound scan image of the CCA. This paper proposes an automatic image segmentation procedure for the measurement of the IMT, avoiding the user dependence and the inter-rater variability. In particular, Radial Basis Function (RBF) Networks are designed and trained by means of the Optimally Pruned-Extreme Learning Machine (OP-ELM) algorithm to classify pixels from a given ultrasound image, allowing the extraction of IMT boundaries. The suggested approach has been validated on a set of 25 ultrasound images by comparing the automatic segmentations with manual tracings.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.