Comput Methods Programs Biomed - Automatic classification of the interferential tear film lipid layer using colour texture analysis.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
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{ signal(2180) analysi(812) frequenc(800) }
{ use(976) code(926) identifi(902) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ assess(1506) score(1403) qualiti(1306) }
{ method(1557) propos(1049) approach(1037) }
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{ 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) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. This papers presents an exhaustive study about the characterisation of the interference phenomena as a texture pattern, using different feature extraction methods in different colour spaces. These methods are first analysed individually and then combined to achieve the best results possible. The principal component analysis (PCA) technique has also been tested to reduce the dimensionality of the feature vectors. The proposed methodologies have been tested on a dataset composed of 105 images from healthy subjects, with a classification rate of over 95% in some cases.

Resumo Limpo

tear film lipid layer heterogen among popul classif depend thick can done use interfer pattern categori propos guillon paper present exhaust studi characteris interfer phenomena textur pattern use differ featur extract method differ colour space method first analys individu combin achiev best result possibl princip compon analysi pca techniqu also test reduc dimension featur vector propos methodolog test dataset compos imag healthi subject classif rate case

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