Inform Health Soc Care - iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

Tópicos

{ featur(1941) imag(1645) propos(1176) }
{ search(2224) databas(1162) retriev(909) }
{ take(945) account(800) differ(722) }
{ concept(1167) ontolog(924) domain(897) }
{ case(1353) use(1143) diagnosi(1136) }
{ visual(1396) interact(850) tool(830) }
{ imag(2675) segment(2577) method(1081) }
{ extract(1171) text(1153) clinic(932) }
{ system(1976) rule(880) can(841) }
{ health(3367) inform(1360) care(1135) }
{ time(1939) patient(1703) rate(768) }
{ process(1125) use(805) approach(778) }
{ problem(2511) optim(1539) algorithm(950) }
{ research(1085) discuss(1038) issu(1018) }
{ can(774) often(719) complex(702) }
{ import(1318) role(1303) understand(862) }
{ blood(1257) pressur(1144) flow(957) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ featur(3375) classif(2383) classifi(1994) }
{ learn(2355) train(1041) set(1003) }
{ algorithm(1844) comput(1787) effici(935) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ control(1307) perform(991) simul(935) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Resumo Limpo

nowaday tradit search engin googl yahoo bing facilit retriev inform format imag result alway use user main due two problem semant keyword taken consider alway possibl establish queri use imag featur issu cover differ domain order develop contentbas imag retriev cbir system expert communiti focuss attent healthcar domain lot visual inform medic analysi avail paper provid solut call ipixel visual search engin involv semant content issu order search digit mammogram ipixel offer possibl retriev mammogram featur use collect intellig implement cbir algorithm propos compar featur similar semant mean also visual featur sens comparison made differ way number region per imag maximum minimum size region per imag averag intens level region ipixel visual search engin support medic communiti differenti diagnos relat diseas breast ipixel visual search engin valid expert healthcar domain radiologist addit expert digit imag analysi

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