دانلود رایگان مقاله روش تشخیص دیسک نوری موثر براساس تکنیک های هوش ازدحامی

Abstract

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.

6. Conclusion

This work further investigated the use of swarm intelligence techniques to detect the optic disc in fundus images. All five techniques were preceded by pre-processing involving many levels of median filtering, background subtraction, mean filtering and masking. The Background Subtraction based Optic Disc Detection algorithm was tested and evaluated. The experimental results con- firmed the superiority of the swarm intelligence techniques to others in detecting intelligence. In particular, the firefly algorithm was experimentally demonstrated to have the highest accuracy and running time, followed closely by the cuckoo search algorithm. We conclude that with suitable pre-processing and parameters, swarm intelligence techniques are very effective in detecting the optic disc accurately and rapidly. A performance study on the effect of varying parameters of the nature inspired algorithms was also presented in this paper. Future work will include evaluating the performance of other swarm intelligence and other optic disc detection methods using the proposed pre-processing method.