Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks

Journal ar
Medical and Biological Engineering and Computing
  • Volumen: 52
  • Número: 2
  • Fecha: 01 February 2014
  • Páginas: 169-181
  • ISSN: 01400118
  • Source Type: Journal
  • DOI: 10.1007/s11517-013-1128-4
  • Document Type: Article
Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness. © 2013 International Federation for Medical and Biological Engineering.

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