Comput Methods Programs Biomed - Segmentation of ultrasound images of the carotid using RANSAC and cubic splines.

Tópicos

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{ method(984) reconstruct(947) comput(926) }
{ estim(2440) model(1874) function(577) }
{ featur(3375) classif(2383) classifi(1994) }
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Resumo

A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary. A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes. The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95% of the far-end boundaries and 73% of the near-end boundaries.

Resumo Limpo

new algorithm propos semiautomat segment nearend farend adventitia boundari common carotid arteri ultrasound imag use random sampl consensus method estim signific cubic spline fit edg map longitudin section consensus geometr model spline evalu new gain function integr respons differ discrimin featur carotid boundari proxim geometr model edg valley shape edg consist orient normal geometr model intens gradient distanc rough estim lumen boundari set longitudin bmode imag common carotid manual segment perform two medic expert use assess perform method imag set taken differ subject plaqu differ class class ii class iv size shape quantit evalu show promis result detect error similar one observ manual segment farend boundari nearend boundari

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