Comput Math Methods Med - Multiple active contours guided by differential evolution for medical image segmentation.

Tópicos

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{ error(1145) method(1030) estim(1020) }
{ activ(1138) subject(705) human(624) }
{ imag(2830) propos(1344) filter(1198) }
{ model(3480) simul(1196) paramet(876) }
{ sampl(1606) size(1419) use(1276) }
{ problem(2511) optim(1539) algorithm(950) }
{ imag(1947) propos(1133) code(1026) }
{ method(1219) similar(1157) match(930) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ care(1570) inform(1187) nurs(1089) }
{ perform(999) metric(946) measur(919) }
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{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

Resumo Limpo

paper present new imag segment method base multipl activ contour guid differenti evolut call macd segment method use differenti evolut polar coordin system increas explor exploit capabl regard classic activ contour model evalu perform propos method set synthet imag complex object gaussian nois deep concav introduc subsequ macd appli dataset sequenti comput tomographi magnet reson imag contain human heart human left ventricl respect final obtain quantit qualit evalu medic imag segment compar region outlin expert set distanc similar metric adopt accord experiment result macd outperform classic activ contour model interact tseng method term effici robust obtain optim control point attain high accuraci segment

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