Local configuration pattern features for age-related macular degeneration characterization classification

Abstract—Abstract Age- related Macular Degeneration(AMD)is an irreversible and chronic medical condition characterized by drusen, Choroidal Neo va scularization (CNV)and Geographic Atrophy(GA). AMD is one of the major causes of visual loss among elderly people.It is caused by the degeneration of cells in the macular which is responsible for central vision.AMD can be dry or wet type,how ever dry AMD is most common.It is classified in to early,intermediate and late AMD.The early detection and treatment may help one to stop the progression of the disease.Automated AMD diagnosis may reduce the screening time of the clinicians. In this work,we have introduced LCP to characterize normal and AMD classes using fund us images. Linear Configuration Coefficients (CC)and Pattern Occurrence(PO)features are extracted from fund us images.These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz.Decision Tree(DT),Nearest Neighbour(k-NN), Naive Bayes(NB),Probabilistic Neural Network (PNN)and Support Vector Machine (SVM)to classify normal and AMD classes.The performance of the system is evaluated using both private(Kasturba Medical Hospital,Manipal,India) and public domain data sets viz.Automated Retinal Image Analysis(ARIA)and Structured Analysis of the Retina(STARE)using ten-fold cross validation.The proposed approach yielded best performance with a highest average accuracy of 97.78%,sensitivity of 98.00% and specificity of 97.50% for STARE data set using 22 significant features.Hence,this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.