Carotid artery (CA) intima-media thickness (IMT) is commonly deemed as one of the risk marker for cardiovascular diseases. The automatic estimation of the IMT on ultrasound images is based on the correct identification of the lumen-intima (LI) and media-adventitia (MA) interfaces. This task is complicated by noise, different morphology and pathology of the carotid artery. In a previous study, we applied four non-linear methods for feature selection on a set of variables extracted from ultrasound carotid images. The main aim was to select those parameters containing the highest amount of information useful to classify the image pixels in the carotid regions they belong to. In this study we present a pixel classifier based on the selected features. Once the pixels classification was correctly performed, the LI and MA interfaces were extracted and compared with two sets of manual-traced profiles.

Automatic carotid segmentation based on pixels classification / Rosati, Samanta; Molinari, Filippo; Balestra, Gabriella. - ELETTRONICO. - (2012). (Intervento presentato al convegno Terzo Congresso Gruppo Nazionale di Bioingegneria tenutosi a Roma nel June 26th-29th 2012).

Automatic carotid segmentation based on pixels classification

ROSATI, SAMANTA;MOLINARI, FILIPPO;BALESTRA, Gabriella
2012

Abstract

Carotid artery (CA) intima-media thickness (IMT) is commonly deemed as one of the risk marker for cardiovascular diseases. The automatic estimation of the IMT on ultrasound images is based on the correct identification of the lumen-intima (LI) and media-adventitia (MA) interfaces. This task is complicated by noise, different morphology and pathology of the carotid artery. In a previous study, we applied four non-linear methods for feature selection on a set of variables extracted from ultrasound carotid images. The main aim was to select those parameters containing the highest amount of information useful to classify the image pixels in the carotid regions they belong to. In this study we present a pixel classifier based on the selected features. Once the pixels classification was correctly performed, the LI and MA interfaces were extracted and compared with two sets of manual-traced profiles.
2012
9788855531825
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2498980
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