Particle Swarm Optimization-Based Approach for Optic Disc Segmentation

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 6; p. 796
• Main Authors: Yi, Junyan, Ran, Ya, Yang, Gang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.06.2022; MDPI
• Subjects: Algorithms; Coordinate transformations; Datasets; Deep learning; exploration area; Extreme values; Glaucoma; Image segmentation; Medical personnel; optic disc segmentation; Optimization; Particle swarm optimization; Searching; Subgroups; optic disc segmentation; subgroups; particle swarm optimization; exploration area
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24060796

Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work.