IEEE Trans Vis Comput Graph - Skeleton Cuts - An Efficient Segmentation Method for Volume Rendering.

Tópicos

{ visual(1396) interact(850) tool(830) }
{ imag(2675) segment(2577) method(1081) }
{ structur(1116) can(940) graph(676) }
{ health(1844) social(1437) communiti(874) }
{ can(981) present(881) function(850) }
{ motion(1329) object(1292) video(1091) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ import(1318) role(1303) understand(862) }
{ data(1737) use(1416) pattern(1282) }
{ use(976) code(926) identifi(902) }
{ system(1050) medic(1026) inform(1018) }
{ state(1844) use(1261) util(961) }
{ general(901) number(790) one(736) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ patient(1821) servic(1111) care(1106) }
{ inform(2794) health(2639) internet(1427) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
{ data(1714) softwar(1251) tool(1186) }
{ perform(999) metric(946) measur(919) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ data(3008) multipl(1320) sourc(1022) }
{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Volume rendering has long been used as a key technique for volume data visualization, which works by using a transfer function to map color and opacity to each voxel. Many volume rendering approaches proposed so far for voxels classification have been limited in a single global transfer function, which is in general unable to properly visualize interested structures. In this paper, we propose a localized volume data visualization approach which regards volume visualization as a combination of two mutually related processes: the segmentation of interested structures and the visualization using a locally designed transfer function for each individual structure of interest. As shown in our work, a new interactive segmentation algorithm is advanced via skeletons to properly categorize interested structures. In addition, a localized transfer function is subsequently presented to assign optical parameters via interested information such as intensity, thickness and distance. As can be seen from the experimental results, the proposed techniques allow to appropriately visualize interested structures in highly complex volume medical data sets.

Resumo Limpo

volum render long use key techniqu volum data visual work use transfer function map color opac voxel mani volum render approach propos far voxel classif limit singl global transfer function general unabl proper visual interest structur paper propos local volum data visual approach regard volum visual combin two mutual relat process segment interest structur visual use local design transfer function individu structur interest shown work new interact segment algorithm advanc via skeleton proper categor interest structur addit local transfer function subsequ present assign optic paramet via interest inform intens thick distanc can seen experiment result propos techniqu allow appropri visual interest structur high complex volum medic data set

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