Microscopic Image Analysis of Nanoparticles by Edge Detection Using Ant Colony Optimization
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 11, Issue 3
Abstract
In this paper, I present an approach for analyzing nanoparticles microscopic images by edge detection using Ant Colony Optimization (ACO) algorithm to obtain a well-connected image edge map. Microscope image analysis of nanoparticles are subject to errors. Initially, the edge map of the image is obtained using various matlab toolbox conventional edge detectors & adaptive thresholding. The end points obtained using such detectors are calculated. The ants are then placed at these points. The movement of the ants is guided by the local variation in the pixel intensity values. The probability factor of only undetected neighboring pixels is taken into consideration while moving an ant to the next probable edge pixel. The two stopping rules are implemented to prevent the movement of ants through the pixel already detected. The method is applied on the atomic force microscope (AFM) images of Cerium Oxide (CeO2) nanoparticles & SEM image of ZnO nanoparticles. The results show that the edges obtained in the images can be used for classification of particles, determining sizes & shapes & also distinguishing particles in agglomerates more precisely
Authors and Affiliations
Shwetabh Singh
Keywords
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