2026-04-14T17:49:50Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/658752025-02-19T13:00:45Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Menchon Lara, Rosa María Bastida Jumilla, María Consuelo Morales Sánchez, Juan Sancho Gómez, José Luis 2024-02-07T08:44:39Z 2024-02-07T08:44:39Z 2013 Med Biol Eng Comput, 52, 169–181 (2014) 0140-0118 https://uvadoc.uva.es/handle/10324/65875 10.1007/s11517-013-1128-4 169 2 181 Medical & Biological Engineering & Computing 52 1741-0444 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. application/pdf eng info:eu-repo/semantics/restrictedAccess Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion SI