A texture-based method for identificaiton of retinal vasculature

Noting the advantages of texture-based features over the structural descriptors of vascular trees, we investigated texture-based features from gray level cooccurrence matrix (GLCM) and various wavelet packet energies to classify retinal vasculature for biometric identification. Wavelet packet energy features were generated by Daubechies, Coiflets and Reverse Biorthogonal wavelets. Two different entropy methods, Shannon and logarithm of energy, were used to prune wavelet packet decomposition trees. Next, wrapper methods were used for classification-guided feature selection. Features were ranked based on area under the receiver operating curves, Bhattacharya, and t-test metrics. Using the ranked lists, wrapper methods were used in conjunction with Naïve Bayesian, k-nearest neighbor (k-NN), and Support Vector Machine (SVM) classifiers. Best results were achieved by using features from Reverse Biorthogonal 2.4 wavelet packet decomposition in conjunction with a nearest neighbor classifier, yielding a 3-fold cross validation accuracy of 99.42% with a sensitivity and specificity of 98.33% and 99.47% respectively.