2026-04-14T17:31:10Zhttps://uvadoc.uva.es/oai/request

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

Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks Menchon Lara, Rosa María Bastida Jumilla, María Consuelo Morales Sánchez, Juan Sancho Gómez, José Luis 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. 2024-02-07T08:44:39Z 2024-02-07T08:44:39Z 2013 info:eu-repo/semantics/article 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 eng info:eu-repo/semantics/restrictedAccess SI