An automated classification framework for glaucoma detection in fundus images using ensemble of dynamic selection methods

Glaucoma is an optic neuropathy, which leads to vision loss and is irreversible due to damage in the optic nerve head mainly caused by increased intra-ocular pressure. Retinal fundus photography facilitates ophthalmologist in detection of glaucoma but is subjective to human intervention and is time-consuming. Computational methods such as image processing and machine learning classifiers can aid in computer-based glaucoma detection which helps in mass screening of glaucoma. In this context, the proposed method develops an automated glaucoma detection system, in the following steps: (i) pre-processing by segmenting the blood vessels using directional filter; (ii) segmenting the region of interest by using statistical features; (iii) extracting the clinical and texture-based features; and (iv) developing ensemble of classifier models using dynamic selection techniques. The proposed method is evaluated on two publically available datasets and 300 fundus images collected from a hospital. The best results are obtained using ensemble of random forest using META-DES dynamic ensemble selection technique, and the average specificity, sensitivity and accuracy for glaucoma detection on hospital dataset are 100%, respectively. For RIM-ONE dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 100%, 93.85% and 97.86%, respectively. For Drishti dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 90%, 100% and 97%, respectively. The quantitative results and comparative study indicate the ability of the developed method, and thus, it can be deployed in mass screening and also as a second opinion in decision making by the ophthalmologist for glaucoma detection.