Automated pharmaceutical tablet coating layer evaluation of optical coherence tomography images

Daniel Markl, Günther Hannesschläger, Stephan Sacher, Michael Leitner, Johannes G. Khinast, Andreas Buchsbaum

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Film coating of pharmaceutical tablets is often applied to influence the drug release behaviour. The coating characteristics such as thickness and uniformity are critical quality parameters, which need to be precisely controlled. Optical coherence tomography (OCT) shows not only high potential for off-line quality control of film-coated tablets but also for in-line monitoring of coating processes. However, an in-line quality control tool must be able to determine coating thickness measurements automatically and in real-time. This study proposes an automatic thickness evaluation algorithm for bi-convex tables, which provides about 1000 thickness measurements within 1 s. Beside the segmentation of the coating layer, optical distortions due to refraction of the beam by the air/coating interface are corrected. Moreover, during in-line monitoring the tablets might be in oblique orientation, which needs to be considered in the algorithm design. Experiments were conducted where the tablet was rotated to specified angles. Manual and automatic thickness measurements were compared for varying coating thicknesses, angles of rotations, and beam displacements (i.e. lateral displacement between successive depth scans). The automatic thickness determination algorithm provides highly accurate results up to an angle of rotation of 30. The computation time was reduced to 0.53 s for 700 thickness measurements by introducing feasibility constraints in the algorithm.

Original languageEnglish
Article number035701
Number of pages12
JournalMeasurement Science and Technology
Issue number3
Publication statusPublished - 2 Feb 2015


  • distortion correction
  • in-line quality control
  • optical coherence tomography
  • pharmaceutical film-coated tablets
  • segmentation

Cite this