A metaheuristic segmentation framework for detection of retinal disorders from fundus images using a hybrid ant colony optimization

Abstract

Imaging modalities play a major role in early detection and diagnosis of various medical conditions related to the patient. Retinal image segmentation has been taken up for investigation in this research paper to efficiently detect the presence of eye disorder which could be indicators of major onset of conditions like hypertension, cataracts, diabetic retinopathy, age-related macular disorders, etc. A machine learning method for classification of given pixels in the search space into regions containing blood vessels and those that do not contain blood vessels is implemented using a three-stage neural classifier in this paper. Prior to classification, an optimization algorithm namely ant colony optimization derived from nature-inspired phenomena is used to provide an optimal feature vector set to set high standards for the neural network based classification approach. The novelty and merits of the paper lie in back tracing of the segmentation process in which optimization is done first on the preprocessed features followed by classification for segmented output on the optimized features. This results in elimination of redundant feature vectors which tend to occupy much memory as well increase the computational overhead on the process. The entire implemented system is automated by the machine learning process and tested on 30 samples, 15 each on DRIVE and STARE databases. Classification rates of nearly 98% on an average scenario have been achieved for segmentation and 96.5% for abnormality detection. The performances have been compared against Bayesian set models and standalone ANN models.