AMIA Annu Symp Proc - Window classification of brain CT images in biomedical articles.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ model(2656) set(1616) predict(1553) }
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{ visual(1396) interact(850) tool(830) }
{ data(2317) use(1299) case(1017) }
{ studi(2440) review(1878) systemat(933) }
{ model(2220) cell(1177) simul(1124) }
{ high(1669) rate(1365) level(1280) }
{ process(1125) use(805) approach(778) }
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{ take(945) account(800) differ(722) }
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{ patient(2837) hospit(1953) medic(668) }
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{ group(2977) signific(1463) compar(1072) }
{ 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) }
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{ patient(1821) servic(1111) care(1106) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.

Resumo Limpo

effect capabl search biomed articl base visual properti articl imag may signific augment inform retriev futur paper present new method classifi window set type brain ct imag window techniqu frequent use evalu ct scan use enhanc contrast particular tissu abnorm type evalu particular provid radiologist enhanc view certain type cranial abnorm skull lesion bone dysplasia usual examin use bone window set illustr biomed articl use bone window imag due inher larg variat imag among articl import propos method robust algorithm attain accuraci classifi imag bone window nonbon window imag data set

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