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dc.contributor.authorMenchón Lara, Rosa María
dc.contributor.authorSancho-Gómez, José-Luis
dc.date.accessioned2024-02-07T08:26:48Z
dc.date.available2024-02-07T08:26:48Z
dc.date.issued2015
dc.identifier.citationNeurocomputing, Volume 151, Part 1, 2015, Pages 161-167.es
dc.identifier.issn0925-2312es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/65873
dc.description.abstractAtherosclerosis is responsible for a large proportion of cardiovascular diseases (CVD), which are the leading cause of death 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. It causes thickening and the reduction of elasticity in the blood vessels. An early diagnosis of this condition is crucial to prevent patients from suffering more serious pathologies (heart attacks and strokes). The evaluation of the Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) in B-mode ultrasound images is considered the most useful tool for the investigation of preclinical atherosclerosis. Usually, it is manually measured by the radiologists. This paper proposes a fully automatic segmentation technique based on Machine Learning and Statistical Pattern Recognition to measure IMT from ultrasound CCA images. The pixels are classified by means of artificial neural networks to identify the IMT boundaries. Moreover, the concepts of Auto-Encoders (AE) and Deep Learning have been included in the classification strategy. The suggested approach is tested on a set of 55 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.titleFully automatic segmentation of ultrasound common carotid artery images based on machine learninges
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1016/j.neucom.2014.09.066es
dc.identifier.publicationfirstpage161es
dc.identifier.publicationlastpage167es
dc.identifier.publicationtitleNeurocomputinges
dc.identifier.publicationvolume151es
dc.peerreviewedSIes
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones


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