An Efficient Decision Support System for Detection of Glaucoma in Fundus Images

This paper proposes a computer aided decision support system for an automated detection of glaucoma in monocular fundus images. Identification of Glaucoma using fundus images involves the measurement of the size, shape of the Optic cup and Neuroretinal rim. Optic Cup detection is a challenging task because of the interweavement of cup with the blood vessels. A new color model technique based on pallor in fundus images using K means clustering is proposed to differentiate between the Optic cup to disc boundary. The method differs by initial optic cup region detection followed by the erasure of blood vessels. In addition to the shape based features, textural features are extracted to better characterize the pathological subjects. Optimal set of features selected by Genetic algorithm are fed as input to Adaptive Neuro fuzzy inference system for classification of images into normal, suspect and abnormal categories. The method has been evaluated on 550 images comprising normal and glaucomatous images. The performance of the proposed technique is compared with Neural Network and SVM Classifier in terms of classification accuracy and convergence time. Experimental results shows that the features used are clinically significant for the accurate detection of glaucoma.