Comput. Biol. Med. - A robust medical image segmentation method using KL distance and local neighborhood information.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ method(1557) propos(1049) approach(1037) }
{ perform(999) metric(946) measur(919) }
{ perform(1367) use(1326) method(1137) }
{ spatial(1525) area(1432) region(1030) }
{ imag(1057) registr(996) error(939) }
{ motion(1329) object(1292) video(1091) }
{ problem(2511) optim(1539) algorithm(950) }
{ intervent(3218) particip(2042) group(1664) }
{ method(2212) result(1239) propos(1039) }
{ featur(1941) imag(1645) propos(1176) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
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{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
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{ data(2317) use(1299) case(1017) }
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{ research(1218) medic(880) student(794) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ time(1939) patient(1703) rate(768) }
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{ decis(3086) make(1611) patient(1517) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we propose an improved Chan-Vese (CV) model that uses Kullback-Leibler (KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity and complex constructions, the performance of level set segmentation is subject to confounding by the presence of nearby structures of similar intensity, preventing it from discerning the exact boundary of the object. Moreover, the CV model cannot usually obtain accurate results in medical image segmentation in cases of optimal configuration of controlling parameters, which requires substantial manual intervention. To overcome the above deficiency, we improve the segmentation accuracy by the usage of KL distance and LNI, thereby introducing the image local characteristics. Performance evaluation of the present method was achieved through experiments on the synthetic images and a series of real medical images. The extensive experimental results showed the superior performance of the proposed method over the state-of-the-art methods, in terms of both robustness and efficiency.

Resumo Limpo

paper propos improv chanves cv model use kullbackleibl kl distanc local neighborhood inform lni due effect heterogen complex construct perform level set segment subject confound presenc nearbi structur similar intens prevent discern exact boundari object moreov cv model usual obtain accur result medic imag segment case optim configur control paramet requir substanti manual intervent overcom defici improv segment accuraci usag kl distanc lni therebi introduc imag local characterist perform evalu present method achiev experi synthet imag seri real medic imag extens experiment result show superior perform propos method stateoftheart method term robust effici

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