Int J Comput Assist Radiol Surg - Automated quality assessment of retinal fundus photos.

Tópicos

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{ assess(1506) score(1403) qualiti(1306) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ signal(2180) analysi(812) frequenc(800) }
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{ sampl(1606) size(1419) use(1276) }
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{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ method(2212) result(1239) propos(1039) }

Resumo

JECTIVE: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation.METHODS: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification.RESULTS: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches.CONCLUSIONS: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.

Resumo Limpo

jectiv autom object fast measur imag qualiti singl retin fundus photo allow stabl reliabl medic evaluationmethod propos techniqu map diagnosisrelev criteria inspir diagnosi procedur base advis eye expert quantit object featur relat imag qualiti independ segment method combin global cluster local sharp textur featur classificationresult test dataset retin fundus imag evalu method given gold standard human observ compar state art approach area roc curv compar outperform state art approach signific pvalu emphas statist differ approachesconclus combin local global imag statist model defin qualiti criteria automat produc reliabl object result determin imag qualiti retin fundus photo

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