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Particle Swarm Optimization for Nano-Particles Extraction from Supporting Materials

Evolutionary computation for image processing is an encouraging research area. Transmission electron microscopy (TEM) images when used to characterize metallic and non-metallic nano-particles (size, morphology, structure, or composition), need such advanced image processing algorithms. This paper presents an efficient evolutionary computational method, particle swarm optimization (PSO), for automatic segmentation of nano-particles. A threshold-based segmentation technique is applied, where image entropy is attacked as a minimization problem to specify local and global thresholds. We are concerned with reducing wrong characterization of nano-particles due to concentration of liquid solutions or supporting material within the acquired image. The obtained results are compared with manual techniques and with previous researches in this area.