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Real-time image segmentation for visual inspection of pharmaceutical tablets

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Abstract

Visual appearance is an important quality factor of pharmaceutical tablets. Due to the vast quantities of produced tablets and high-quality requirements, pharmaceutical companies are interested in employing automated systems for real-time visual tablet inspection with the speeds of up to 100 tablets per second. Such systems require reliable tablet manipulation, illumination, image acquisition, tablet image analysis, classification, and sorting system. Tablet image segmentation, in which each tablet image is partitioned into the tablet region and background, is the first and very important step in tablet image analysis. It this paper, we propose a novel real-time segmentation method for grey-level images that is based on border tracking. The proposed method was designed to be accurate, robust, and computationally undemanding. The performances of the method were objectively assessed on a large number of simulated and real tablet images. The obtained results indicated high reliability, accuracy, and speed. The 100% reliability was obtained for segmentation of real images of pharmaceutical tablets, while the segmentation times were no more than 1.5 ms or 15% of the whole time available for tablet image analysis. As such, the proposed method proved feasible for real-time visual quality inspection of pharmaceutical tablets. Based on just a few assumptions that are usually fulfilled, the method may be a valuable segmentation tool for many other visual quality inspection applications.

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References

  1. Berman A.: Reducing medication errors through naming, labeling, and packaging. J. Med. Syst. 28, 9–29 (2004)

    Article  Google Scholar 

  2. FDA Code of Federal Regulations (21CFR206) Imprinting of solid oral dosage form drug products for human use. Available at http://www.accessdata.fda.gov/

  3. Newman T.S., Jain A.K.: A survey of automated visual inspection. Comput. Vis. Image Underst. 61, 231–262 (1995)

    Article  Google Scholar 

  4. Malamas E.N., Petrakis E.G.M., Zervakis M., Petit L., Legat J.D.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21, 171–188 (2003)

    Article  Google Scholar 

  5. Deutschl, E., Rinnhofer, A.: Tablet quality assurance in real time. In: Proocedings of the 14th International Conference on Pattern Recognition, vol. 2, Brisbane, Australia, pp. 1731–1734 (1998)

  6. Bukovec M., Špiclin Ž., Pernuš F., Likar B.: Automated visual inspection of imprinted pharmaceutical tablets. Meas. Sci. Technol. 18, 2921–2930 (2007)

    Article  Google Scholar 

  7. Derganc J., Likar B., Bernard R., Tomaževič D., Pernuš F.: Real-time automated visual inspection of color tablets in pharmaceutical blisters. Real Time Imaging 9, 113–124 (2003)

    Article  Google Scholar 

  8. Špiclin, Ž., Bukovec, M., Pernuš, F., Likar, B.: Image registration for visual inspection of imprinted pharmaceutical tablets. Mach. Vis. Appl. doi:10.1007/s00138-007-0104-0

  9. Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis, and Machine Vision. PWS Publishing, Brooks/Cole Publishing Company, Pacific Grove, California (1998)

    Google Scholar 

  10. Gatos B., Pratikakis I., Perantonis S.J.: Adaptive degraded document image binarization. Pattern Recognit. 39, 317–327 (2006)

    Article  MATH  Google Scholar 

  11. Sezgin M., Sankur B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electr. Imaging 13, 146–168 (2004)

    Article  Google Scholar 

  12. Yanowitz S., Bruckstein A.: A new method for image segmentation. Comput. Vis. Graph 46, 82–95 (1989)

    Article  Google Scholar 

  13. Boskovitz V., Guterman H.: An adaptive neuro-fuzzy system for automatic image segmentation and edge detection. IEEE T Fuzzy Syst. 10, 247–262 (2002)

    Article  Google Scholar 

  14. Navon E., Miller O., Averbuch A.: Color image segmentation based on adaptive local thresholds. Image Vis. Comput. 23, 69–85 (2005)

    Article  Google Scholar 

  15. Russ J.C.: The Image Processing Handbook. CRC Press, Boca Raton (2007)

    MATH  Google Scholar 

  16. Likar B., Antoine J., Viergever M.A., Pernuš F.: Retrospective shading correction based on entropy minimization. J. Microsc. 197, 285–295 (2000)

    Article  Google Scholar 

  17. Tomaževič D., Likar B., Pernuš F.: Comparative evaluation of retrospective shading correction methods. J. Microsc. 208, 212–223 (2002)

    Article  MathSciNet  Google Scholar 

  18. Illingworth J., Kittler J.: A survey of the Hough transform. Comput. Vis. Graph 44, 87–116 (1988)

    Article  Google Scholar 

  19. Zhang S.C., Liu Z.Q.: A robust, real-time ellipse detector. Pattern Recognit. 38, 273–287 (2005)

    Article  MATH  Google Scholar 

  20. Kass M., Witkin A., Terzopoulos D.: Snakes—active contour models. Int. J. Comput. Vis. 1, 321–331 (1987)

    Article  Google Scholar 

  21. Williams D.J., Shah M.: A fast algorithm for active contours and curvature estimation. CVGIP Image Underst. 55, 14–26 (1992)

    Article  MATH  Google Scholar 

  22. Xu C.Y., Prince J.L.: Snakes, shapes, and gradient vector flow. IEEE T Image Process. 7, 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  23. Cootes T.F., Taylor C.J., Cooper D.H., Graham J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)

    Article  Google Scholar 

  24. Bresson X., Esedoglu S., Vandergheynst P., Thiran J.P., Osher S.: Fast global minimization of the active Contour/Snake model. J. Math. Imaging Vis. 28, 151–167 (2007)

    Article  MathSciNet  Google Scholar 

  25. Precioso F., Barlaud M., Blu T., Unser M.: Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm. IEEE Trans. Image Process. 14, 910–924 (2005)

    Article  Google Scholar 

  26. Adams R., Bischof L.: Seeded region growing. IEEE T Pattern Anal. 16, 641–647 (1994)

    Article  Google Scholar 

  27. Chang Y.L., Li X.B.: Adaptive image region-growing. IEEE T Image Process. 3, 868–872 (1994)

    Article  MathSciNet  Google Scholar 

  28. Vincent L., Soille P.: Watersheds in digital spaces—an efficient algorithm based on immersion simulations. IEEE T Pattern Anal. 13, 583–598 (1991)

    Article  Google Scholar 

  29. Fan H.P., Zeng G.H., Body M., Hacid M.S.: Seeded region growing: an extensive and comparative study. Pattern Recognit. Lett. 26, 1139–1156 (2005)

    Article  Google Scholar 

  30. Wan S.Y., Higgins W.E.: Symmetric region growing. IEEE Trans. Image Process. 12, 1007–1015 (2003)

    Article  Google Scholar 

  31. Pavlidis T.: Algorithms for Graphical and Image Processing. Springer, Berlin (1982)

    Google Scholar 

  32. Duda R., Hart P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  33. Krause E.F.: Taxicab Geometry: An Adventure in Non-Euclidean Geometry. Dover Publications, Dover (1987)

    Google Scholar 

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Correspondence to Miha Možina.

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Možina, M., Tomaževič, D., Pernuš, F. et al. Real-time image segmentation for visual inspection of pharmaceutical tablets. Machine Vision and Applications 22, 145–156 (2011). https://doi.org/10.1007/s00138-009-0218-7

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  • DOI: https://doi.org/10.1007/s00138-009-0218-7

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