Effective optic disc detection method based on swarm intelligence techniques and novel pre-processing steps

Display Omitted Proposing a work that is competitive with state-of-art optic disc detection methods.Describing a novel pre-processing method that improves optic disc detection accuracy.The novel use of four swarm intelligence algorithms (artificial bee colony, particle swarm optimization, bat algorithm, and cuckoo search) for optic disc detection.Providing accuracy, consistency and speed comparison between five swarm algorithms.Providing high performance parameters for swarm intelligence algorithms for optic disc detection and performance study on each parameter and its effect on the accuracy. Diabetic retinopathy affects the vision of a significant fraction of the population worldwide. Retinal fundus images are used to detect the condition before vision loss develops to enable medical interventions. Optic disc detection is an essential step for the automatic detection of the disease. Several techniques have been introduced in the literature to detect the optic disc with different performance characteristics such as speed, accuracy and consistency. For optic disc detection, a nature-inspired algorithm called swarm intelligence has been shown to have clear superiority in terms of speed and accuracy compared to traditional detection algorithms. We therefore further investigated and compared several swarm intelligence techniques. Our study focused on five popular swarm intelligence algorithms: artificial bee colony, particle swarm optimization, bat algorithm, cuckoo search and firefly algorithm. This work also featured a novel pre-processing scheme that enhances the detection accuracy of the swarm techniques by making the optic disc region the highest grayscale value in the image. The pre-processing involves multiple stages of background subtraction, median filtering and mean filtering and is named Background Subtraction-based Optic Disc Detection (BSODD). The best result was obtained by combining our pre-processing technique, firefly algorithm and the parameters used for the algorithm. The obtained accuracy was superior to the other tested algorithms and published results in the literature. The accuracy of the firefly algorithm was 100%, 100%, 98.82% and 95% when using the DRIVE, DiaRetDB1, DMED and STARE databases, respectively.