  In this paper, we have proposed a new glaucoma classification approach that
employs a wavelet neural network (WNN) on optimally enhanced retinal images
features. To avoid tedious and error prone manual analysis of retinal images by
ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust
diagnosis. Our objective is to introduce a CAD system with a fresh approach.
Retinal image quality improvement is attempted in two phases. The retinal image
preprocessing phase improves the brightness and contrast of the image through
quantile based histogram modification. It is followed by the image enhancement
phase, which involves multi scale morphological operations using image specific
dynamic structuring elements for the retinal structure enrichment. Graph based
retinal image features in terms of Local Graph Structures (LGS) and Graph
Shortest Path (GSP) statistics are extracted from various directions along with
the statistical features from the enhanced retinal dataset. WNN is employed to
classify glaucoma retinal images with a suitable wavelet activation function.
The performance of the WNN classifier is compared with multilayer perceptron
neural networks with various datasets. The results show our approach is
superior to the existing approaches.
