Boron Nanoparticle Image Analysis using Machine Learning Algorithms


  • Parashuram Bannigidad
  • Namita Potraj
  • Prabhuodeyara. M
  • Gurubasavaraj
  • Lakkappa.B.Anigol



Boron; Synthesization; Characterization; Nanoparticle; Image analysis; Image segmentation; TEM; FCM; K-Means.


The effort of digital image processing involves efficient computation aimed at developing an economical, faster
and more accurate, and cost-effective automated system. The objective of this paper is to ascertain and categorize
Boron nanoparticles (BNP) using digital image processing techniques. The spatial features are unsheathed from
the Boron nanoparticle Transmission Electron Microscope (TEM) images using different segmentation
techniques, namely; Fuzzy C -means (FCM) and K-means. The size of Boron nanoparticles is determined and
categorized based on the area(size) in the microsize.The synthesization and characterization of Boron
nanoparticles play an important role as an elementary procedure for the formation of Boron nanoparticles. The
results are analyzed, interpreted and comparison is done with the manual values to observe the efficacy of the
results. It is observed that the K-means segmentation technique yields a smaller amountof error (5.87%) as
compared with Fuzzy C-mean(16.78%). Hence, it is considered that the K-Means is the most relevant
segmentation technique for Boron nanoparticle image analysis and categorization. The statistical test of
significance is applied using the Chi-square testing method (at 5% of significance level) to check the relationship
between the manual results and the algorithm results.The proposed study also establishes collaborative research
work between Chemistry and Computer Science departments to develop computational research on these




How to Cite

Parashuram Bannigidad, Namita Potraj, Prabhuodeyara. M, Gurubasavaraj, & Lakkappa.B.Anigol. (2022). Boron Nanoparticle Image Analysis using Machine Learning Algorithms. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 4(1).