Comput Methods Programs Biomed - Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.

Tópicos

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Resumo

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.

Resumo Limpo

prolif diabet retinopathi pdr condit carri high risk sever visual impair hallmark pdr neovascularis growth abnorm new vessel paper describ autom method detect new vessel retin imag two vessel segment approach appli use standard line oper novel modifi line oper latter design reduc fals respons nonvessel edg generat binari vessel map hold vital inform must process separ achiev dual classif system local morpholog featur measur binari vessel map produc two separ featur set independ classif perform featur set use support vector machin svm classifi system combin individu classif outcom produc final decis sensit specif result use dataset imag respect per patch basi respect per imag basi

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