Optic Disc Segmentation with Kapur-ScPSO Based Cascade Multithresholding

The detection of significant retinal regions (segmentation) constitutes an indispensible need for computer aided diagnosis of retinal based diseases. At this point, image segmentation algorithm is wanted to be quick in order to spare time for feature selection and classification parts. In this paper, we deal with the fast and accurate segmentation process of optic discs in retinal images. For this purpose, a cascade multithresholding (CMT) process is proposed by a novel optimization algorithm (Scout Particle Swarm Optimization) and an efficient cost function (Kapur). Scout Particle Swarm Optimization (ScPSO) is originated from Particle Swarm Optimization (PSO) and improves standard PSO by using a necessary part taken from Artificial Bee Colony (ABC) Optimization. In other words, the most important handicap of PSO (regeneration of useless particles) is eliminated via the formation of ScPSO that can be obtained by adding the scout bee phase from ABC into standard PSO. In this study, this novel method (ScPSO) constitutes the optimization part of multithresholding process. Kapur function is preferred as being the cost function to be used in ScPSO, since Kapur provides low standard deviations on output of optimization based multithresholding techniques in literature. In this manner, a well-combined structure (Kapur-ScPSO) is generated for cascade multithresholding. Optic disc images taken from DRIVE database are used for statistical and visual comparison. As a result, Kapur-ScPSO based CMT can define the optic disc quickly (7-8 s) with the rates of 77.08 % precision, 57.89 % overlap and 95.59 % accuracy.