Comput. Biol. Med. - Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.


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Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Resumo Limpo

agerel macular degener amd one major caus vision loss blind age popul current cure amd howev earli detect subsequ treatment may prevent sever vision loss slow progress diseas amd can classifi two type dri wet amd peopl macular degener most affect dri amd earli symptom amd format drusen yellow pigment lesion identifi manual inspect fundus imag ophthalmologist time consum tiresom process henc autom diagnosi amd screen tool can aid clinician diagnosi signific studi propos autom dri amd detect system use various entropi shannon kapur renyi yager higher order spectra hos bispectra featur fraction dimens fd gabor wavelet featur extract greyscal fundus imag featur rank use ttest kullbacklieb diverg kld chernoff bound bhattacharyya distanc cbbd receiv oper characterist roc curvebas wilcoxon rank method order select optimum featur classifi normal amd class use naiv bay nb knearest neighbour knn probabilist neural network pnn decis tree dt support vector machin svm classifi perform propos system evalu use privat kasturba medic hospit manip india autom retin imag analysi aria structur analysi retina stare dataset propos system yield highest averag classif accuraci optim rank featur use svm classifi privat aria stare dataset respect autom amd detect system can use mass fundus imag screen aid clinician make better use expertis select imag requir examin

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