RT info:eu-repo/semantics/article
T1 Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks
A1 Menchón Lara, Rosa María
A1 Bastida-Jumilla, María-Consuelo
A1 Morales-Sánchez, Juan
A1 Sancho-Gómez, José-Luis
AB 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.
SN 0140-0118
YR 2013
FD 2013
LK https://uvadoc.uva.es/handle/10324/65875
UL https://uvadoc.uva.es/handle/10324/65875
LA eng
NO Med Biol Eng Comput, 52, 169–181 (2014)
DS UVaDOC
RD 27-nov-2024