Silver Nanoparticle (AgNps) Image Analysis using Digital Image Processing Techniques

Abstract

Digital image analysis yields an effective mechanism for qualitative exploration in advanced material science and biological studies. An automated image analysis reduces the tiresome work involved in the traditional method to determine more precise quantitative analysis and these methods furnish an important quantitative mechanism to analyze the size and structural features of AgNO3 metal and metal oxide nanoparticles. An Automated microscopic image analysis serves as an effective and efficient mechanism in material science and in nanotechnological studies. The objective of this paper is to determine and categorize Silver (Ag) nanoparticles using digital image processing techniques. The geometric features are extracted from the Ag nanoparticle FESEM images using different segmentation techniques, namely; Fuzzy C-Means (FCM) and K-Means. The size of the nanoparticles is determined and classified based on the nano applications. The Green synthesis model is used to obtain AgNO3 FESEM images to measure the size and shape variance depending on the pH value. The variation in the proposed and manual results is referred to as error. It is observed that the average error for the characteristics of the segmented nanoparticles using Fuzzy C-Means is 14.28% and with K-Means it is 209.47%. The analysis predicts that the error in using Fuzzy C- Means is the least, as compared to K- means method and thus, it is considered the most appropriate segmentation technique among the two methods mentioned in the present study to segment the experimental Ag nanoparticles FESEM images. The proposed results are analyzed, interpreted, and compared with manual values, which proves the effectiveness of the proposed method.