J Med Syst - A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ inform(2794) health(2639) internet(1427) }
{ spatial(1525) area(1432) region(1030) }
{ high(1669) rate(1365) level(1280) }
{ method(2212) result(1239) propos(1039) }
{ patient(2315) diseas(1263) diabet(1191) }
{ framework(1458) process(801) describ(734) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ system(1976) rule(880) can(841) }
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{ model(2220) cell(1177) simul(1124) }
{ howev(809) still(633) remain(590) }
{ detect(2391) sensit(1101) algorithm(908) }
{ take(945) account(800) differ(722) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
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{ ehr(2073) health(1662) electron(1139) }
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{ blood(1257) pressur(1144) flow(957) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ time(1939) patient(1703) rate(768) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

Resumo Limpo

medic imag affect intens inhomogen rather nois outlier great impact effici regionbas imag segment method reli homogen intens region interest meanwhil initi configur control paramet affect perform level set segment address problem paper propos new hybrid method integr local regionbas level set method variat fuzzi cluster specif take inform fusion approach base coarsetofin framework seamless fuse local spatial inform gray level inform inform local regionbas level set method also control paramet level set direct comput fuzzi cluster result approach valuabl benefit autom need prior knowledg region interest roi robust intens inhomogen automat adjust control paramet insensit initi satisfactori accuraci contribut paper provid advantag togeth propos yet inhomogen medic imag propos method test sever medic imag differ modal perform evalu experiment result approv effect segment medic imag comparison similar method

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