Comput Math Methods Med - Multiple active contours driven by particle swarm optimization for cardiac medical image segmentation.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ perform(999) metric(946) measur(919) }
{ first(2504) two(1366) second(1323) }
{ imag(2830) propos(1344) filter(1198) }
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{ featur(1941) imag(1645) propos(1176) }
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{ model(3404) distribut(989) bayesian(671) }
{ method(1557) propos(1049) approach(1037) }
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{ research(1218) medic(880) student(794) }
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Resumo

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Resumo Limpo

paper present novel imag segment method base multipl activ contour driven particl swarm optim macpso propos method use particl swarm optim polar coordin system increas energyminim capabl respect tradit activ contour model first stage evalu robust propos method set synthet imag contain object sever concav gaussian nois present subsequ macpso use segment human heart human left ventricl dataset sequenti comput tomographi magnet reson imag respect final assess perform medic imag segment respect region outlin expert graph cut method object quantifi set distanc similar metric adopt experiment result demonstr macpso outperform tradit activ contour model term segment accuraci stabil

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