IEEE J Biomed Health Inform - Histology image retrieval in optimised multi-feature spaces.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ search(2224) databas(1162) retriev(909) }
{ visual(1396) interact(850) tool(830) }
{ method(1219) similar(1157) match(930) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(3008) multipl(1320) sourc(1022) }
{ system(1976) rule(880) can(841) }
{ featur(3375) classif(2383) classifi(1994) }
{ general(901) number(790) one(736) }
{ import(1318) role(1303) understand(862) }
{ perform(1367) use(1326) method(1137) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ signal(2180) analysi(812) frequenc(800) }
{ sequenc(1873) structur(1644) protein(1328) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ risk(3053) factor(974) diseas(938) }
{ sampl(1606) size(1419) use(1276) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ can(774) often(719) complex(702) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ 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) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Content based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in histology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to automatically combine heterogeneous visual features for histology image retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated to multiple query images. The core of this approach is a multi-objective learning method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimisation problem, and a multi-objective optimisation strategy is employed in order to handle potential contradictions in the query images associated to the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content based histology image retrieval by using an appropriately defined multi-feature fusion model, which takes careful consideration of the structure and distribution of visual features.

Resumo Limpo

content base histolog imag retriev system shown great potenti support decis make clinic activ teach biolog research content base imag retriev featur combin play key role aim enhanc descript power visual featur correspond semant meaning queri particular valuabl histolog imag analysi intellig mechan need interpret vari tissu composit architectur histolog concept paper present approach automat combin heterogen visual featur histolog imag retriev aim obtain repres fusion model particular keyword associ multipl queri imag core approach multiobject learn method aim understand optim visualsemant match function joint consid differ prefer group queri imag task pose optimis problem multiobject optimis strategi employ order handl potenti contradict queri imag associ keyword experi perform two differ collect histolog imag result show possibl improv system content base histolog imag retriev use appropri defin multifeatur fusion model take care consider structur distribut visual featur

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