Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.

A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining andencoding the Daubechies wavelet decomposed center–neighbour pixel relationshipin the local texture. RDbW features are applied in spatial alignment(registration) of multimodal medical images using a Procrustes analysis(PA)‐based affine transformation function and the registered images are furtherfused by employing a wavelet‐based fusion method. A significant amount ofexperiments is conducted and the registration and fusion accuracy of theproposed feature descriptor is compared with the prominent existing methods suchas local binary patterns (LBP), local tetra pattern (LTrP), local diagonalextrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP).Experimental results show the present registration method improves the averageregistration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP,and LBP, respectively. Further, the fusion results of the current approachexhibit an average improvement in entropy by 11%, standard deviation by 6% edgestrength by 12%, sharpness by 23%, and average gradient by 16% when comparedwith all other feature descriptors used for registering the images. Conceptspresented here can be used widely in analysing the combined information presentin multimodal medical images.