Med Biol Eng Comput - NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency.

Tópicos

{ imag(1947) propos(1133) code(1026) }
{ perform(1367) use(1326) method(1137) }
{ state(1844) use(1261) util(961) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ network(2748) neural(1063) input(814) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
{ studi(1410) differ(1259) use(1210) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ cancer(2502) breast(956) screen(824) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
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{ method(1219) similar(1157) match(930) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
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{ system(1050) medic(1026) inform(1018) }
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{ compound(1573) activ(1297) structur(1058) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
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{ data(2317) use(1299) case(1017) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the 'max selection' rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.

Resumo Limpo

articl novel multimod medic imag fusion mif method base nonsubsampl contourlet transform nsct pulsecoupl neural network pcnn present propos mif scheme exploit advantag nsct pcnn obtain better fusion result sourc medic imag first decompos nsct lowfrequ subband lfss fuse use max select rule fuse highfrequ subband hfss pcnn model util modifi spatial frequenc nsct domain input motiv pcnn coeffici nsct domain larg fire time select coeffici fuse imag final invers nsct insct appli get fuse imag subject well object analysi result comparison stateoftheart mif techniqu show effect propos scheme fuse multimod medic imag

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