Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

Liang Zhao; Brendan Odigwe; Susan Lessner; Daniel G. Clair; Firas Mussa; Homayoun Valafar

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

Image and Video Processing 2019-12-19 v1 Computer Vision and Pattern Recognition

Authors: Liang Zhao , Brendan Odigwe , Susan Lessner , Daniel G. Clair , Firas Mussa , Homayoun Valafar

Abstract

We report an object tracking algorithm that combines geometrical constraints, thresholding, and motion detection for tracking of the descending aorta and the network of major arteries that branch from the aorta including the iliac and femoral arteries. Using our automated identification and analysis, arterial system was identified with more than 85% success when compared to human annotation. Furthermore, the reported automated system is capable of producing a stenosis profile, and a calcification score similar to the Agatston score. The use of stenosis and calcification profiles will lead to the development of better-informed diagnostic and prognostic tools.

Keywords

object tracking femoral artery calcification machine learning stenosis agatston score

Cite

@article{arxiv.1912.06010,
  title  = {Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques},
  author = {Liang Zhao and Brendan Odigwe and Susan Lessner and Daniel G. Clair and Firas Mussa and Homayoun Valafar},
  journal= {arXiv preprint arXiv:1912.06010},
  year   = {2019}
}

Comments

6 pages, submitted for consideration to CSCI 2016

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

Abstract

Keywords

Cite

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