Adaptive Background Correction of Crystal Image Datasets: Towards Automated Process Control

Luke Kiernan, Ian Jones, Lauri Kurki, Patrick J. Cullen, Toufic El Arnaout

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


Improving the data descriptor calculation of crystal’s physical properties requires sophisticated imaging techniques and algorithms. It has been possible to construct 2D population balance models benefiting from characteristic measurements of both crystal’s length and width, compared to the single representative sizes used in 1D models. Our aim is to ameliorate the procedure of determining shape (and not only size) factors, in an automated fashion and directly from the process, for implementation in future models. Here, approaches suitable for real-time applications were employed including engineered imaging sensors and adaptive algorithms. We described the latter in detail for varying 2D image datasets. Their basic concept is similar. Each is applicable to an entire dataset, thus demonstrating efficacy for a variety of particle environments. While the challenge of particle segmentation for higher concentrations was not scrutinized here, this approach reduced processing time, steps and supervision, for the benefit of certain applications requiring process monitoring and automation
Original languageEnglish
Article number48
JournalSensing and Imaging
Issue number1
Publication statusPublished - 1 Dec 2020
MoE publication typeA1 Journal article-refereed


  • Adaptive background correction
  • Analytical technology
  • Crystallization imaging
  • Particle engineering


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