Swarm Optimization Techniques for Segmenting Gel Electrophoresis Images

Gel Electrophoresis (GE) are discussed as the main tool to dissociate DNA sequences. It helps in analyzing the genome such that each image resulting from it consists of lanes that include several bands. Image segmentation plays the foremost role in image processing. It helps in producing accurate results in medical diagnosis. Image segmentation works by dividing an image into regions that cover the full image. Image segmentation methods can be implemented, but still have certain defects that cannot produce accurate results. On the other hand, Swarm Optimization methods produce results with high efficiency in image segmentation. In this study, swarm optimization techniques for image segmentation are proposed. The proposed technique depends on applying different segmentation methods as Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) is an extensively used in computer science considered a simple and easy algorithm to implement. It also depends on swarm intelligence. PSO useful in image segmentation because the result is more exact and efficient. Furthermore, Darwinian PSO (DPSO) and Fractional Order Darwinian PSO (FODPSO) produced precise results. The efficiency of the proposed approach is compared with other by computing image quality measurement parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and others. The proposed technique, especially FODPSO produces more accurate results to segment GE image.