Abstract

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 μm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness.

© 2014 Optical Society of America

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References

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  1. S. Bredeau and L. Federzoni, “Multilayer: a large scale production of micro devices via new rolled multi material layered 3D shaping technology,” in Proceedings of the 4 M/ICOMM 2009 Conference, V. Saile, K. Ehmann, and S. Dimov, eds. (Karlsruhe, 2009), pp. 419–422.
  2. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
    [CrossRef]
  3. D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007).
    [CrossRef]
  4. M. D. Duncan, M. Bashkansky, and J. Reintjes, “Subsurface defect detection in materials using optical coherence tomography,” Opt. Express 2, 540–545 (1998).
    [CrossRef]
  5. R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012).
    [CrossRef]
  6. J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
    [CrossRef]
  7. J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.
  8. M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
    [CrossRef]
  9. V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and W. Drexler, “Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express 18, 14730–14744 (2010).
    [CrossRef]
  10. I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
    [CrossRef]
  11. A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
    [CrossRef]
  12. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
    [CrossRef]
  13. A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.
  14. Thorlabs, “Swept-source OCT systems,” Thorlabs Sweden AB, Mölndalsvägen 3, 400 20 Gothenburg, Sweden. http://www.thorlabs.de/newgrouppage9.cfm?objectgroup_id=2098 .
  15. U. Sharma, E. W. Chang, and S. H. Yun, “Long-wavelength optical coherence tomography at 1.7  μm for enhanced imaging depth,” Opt. Express 16, 19712–19723 (2008).
    [CrossRef]
  16. Swerea IVF, “Ceramic materials,” Swerea IVF, Mölndal (head office), P. O. Box 104, SE-431 22 Mölndal, Sweden. http://swerea.se/en/Start2/Working-Areas/Ceramics/ .
  17. R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.
  18. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).
  19. P. Ekberg, “Ultra precision metrology—the key for mask lithography and manufacturing of high definition displays” (Licentiate Thesis, 2011), pp. 17–25.
  20. M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
    [CrossRef]
  21. L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.
  22. T. Doiron and J. Beers, The Gauge Block Handbook (National Institute of Standards and Technology, 2009).
  23. Zygo NewView7300 3D optical surface profiler, http://www.zygo.com/?/met/profilers/newview7000/ .

2012

2011

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

2010

2009

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
[CrossRef]

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

2008

2007

D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007).
[CrossRef]

2006

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

2003

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
[CrossRef]

1999

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
[CrossRef]

1998

1988

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
[CrossRef]

Abramoff, M. D.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Bashkansky, M.

Beers, J.

T. Doiron and J. Beers, The Gauge Block Handbook (National Institute of Standards and Technology, 2009).

Bizheva, K.

Bizheva, K. K.

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Bloch, I.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Bredeau, S.

S. Bredeau and L. Federzoni, “Multilayer: a large scale production of micro devices via new rolled multi material layered 3D shaping technology,” in Proceedings of the 4 M/ICOMM 2009 Conference, V. Saile, K. Ehmann, and S. Dimov, eds. (Karlsruhe, 2009), pp. 419–422.

Bruynooghe, M.

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

Burns, T. L.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Chang, E. W.

Clausi, D. A.

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
[CrossRef]

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Doiron, T.

T. Doiron and J. Beers, The Gauge Block Handbook (National Institute of Standards and Technology, 2009).

Drexler, W.

Duncan, M. D.

Eichel, J. A.

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Ekberg, P.

R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012).
[CrossRef]

P. Ekberg, “Ultra precision metrology—the key for mask lithography and manufacturing of high definition displays” (Licentiate Thesis, 2011), pp. 17–25.

Fard, A.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Federzoni, L.

S. Bredeau and L. Federzoni, “Multilayer: a large scale production of micro devices via new rolled multi material layered 3D shaping technology,” in Proceedings of the 4 M/ICOMM 2009 Conference, V. Saile, K. Ehmann, and S. Dimov, eds. (Karlsruhe, 2009), pp. 419–422.

Fercher, A. F.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
[CrossRef]

Fieguth, P. W.

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Fraschini, M.

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

Fu, G.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Garvin, M. K.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Ghorbel, I.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Goda, K.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Golonka, L.

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

Hermann, B.

Hitzenberger, C. K.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
[CrossRef]

Hofer, B.

Jalali, B.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Jurków, D.

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Kajic, V.

Kass, M.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
[CrossRef]

Kirillin, M.

R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012).
[CrossRef]

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Lasser, T.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
[CrossRef]

Malecha, K.

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Malik, O.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Marshall, D.

Masek, M.

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

Mattsson, L.

R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012).
[CrossRef]

L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Mishra, A.

Mishra, A. K.

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Paques, M.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Považay, B.

Quach, A.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Reintjes, J.

Roos, A.

Rosin, P. L.

Rossant, F.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Russell, S. R.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Schmitt, J. M.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
[CrossRef]

Schneider, J.

L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.

Schulze, V.

L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.

Sergeeva, E.

Sharma, U.

Sonka, M.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Stifter, D.

D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007).
[CrossRef]

Su, R.

R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012).
[CrossRef]

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

Terzopoulos, D.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
[CrossRef]

Tick, S.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Wang, C.

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Wirth, M.

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

Witkin, A.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
[CrossRef]

Wong, A.

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

Wu, X.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Xiang, S. H.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
[CrossRef]

Yun, S. H.

Yung, K. M.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
[CrossRef]

Appl. Phys. B

D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007).
[CrossRef]

EURASIP J. Adv. Sig. Pr.

M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006).
[CrossRef]

IEEE Trans. Med. Imaging

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[CrossRef]

Int. J. Comput. Vis.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988).
[CrossRef]

J. Biomed. Opt.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
[CrossRef]

Opt. Express

Pattern Recogn.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[CrossRef]

Rep. Prog. Phys.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003).
[CrossRef]

Other

S. Bredeau and L. Federzoni, “Multilayer: a large scale production of micro devices via new rolled multi material layered 3D shaping technology,” in Proceedings of the 4 M/ICOMM 2009 Conference, V. Saile, K. Ehmann, and S. Dimov, eds. (Karlsruhe, 2009), pp. 419–422.

J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.

Swerea IVF, “Ceramic materials,” Swerea IVF, Mölndal (head office), P. O. Box 104, SE-431 22 Mölndal, Sweden. http://swerea.se/en/Start2/Working-Areas/Ceramics/ .

R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

P. Ekberg, “Ultra precision metrology—the key for mask lithography and manufacturing of high definition displays” (Licentiate Thesis, 2011), pp. 17–25.

L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.

T. Doiron and J. Beers, The Gauge Block Handbook (National Institute of Standards and Technology, 2009).

Zygo NewView7300 3D optical surface profiler, http://www.zygo.com/?/met/profilers/newview7000/ .

A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.

Thorlabs, “Swept-source OCT systems,” Thorlabs Sweden AB, Mölndalsvägen 3, 400 20 Gothenburg, Sweden. http://www.thorlabs.de/newgrouppage9.cfm?objectgroup_id=2098 .

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Figures (14)

Fig. 1.
Fig. 1.

SEM image of the prepared cross section of the sintered alumina sample.

Fig. 2.
Fig. 2.

Geometrical layout of sample stack. Marked cross sections 1 and 2 correspond to OCT imaging planes.

Fig. 3.
Fig. 3.

OCT B-scan of cross section 2 (marked in Fig. 2) and its corresponding average A-scan. The white bar corresponds to 150 μm in the x-direction. Optical distance scaled by the refractive index of alumina applies in the z-direction in the A-scan profile. Image obtained by the Thorlabs OCT.

Fig. 4.
Fig. 4.

Steps of the new OCT boundary detection algorithm.

Fig. 5.
Fig. 5.

Images describing the different steps in the algorithm. A Input OCT image. B The result after extracting longest ridges in x-direction. C The result after extracting longest ridges in z-direction. D The logical connection map, D=B+C. E Enlargement of the connection map marked with a white rectangle in D. F Final logical template of ridges after merging and cleaning process. The vertical bar in A represents a 150 μm optical distance in the material.

Fig. 6.
Fig. 6.

Principle of the calculation of the projection vector Vxi,j.

Fig. 7.
Fig. 7.

Eight pixel neighborhood of a ridge pixel i, j (marked with a white square) belonging to a ridge in x-direction. The black arrows are the gradient vectors, originating at the centers of these eight pixels and pointing toward the locations of the local maxima, which are marked with red dots. The red crosshair that is calculated as the center of gravity of the red dots is the final sub-pixel location of the ridge pixel at location i, j.

Fig. 8.
Fig. 8.

Result after the sub-pixel-precision location of each pixel in the template shown in Fig. 5F has been calculated. A few false ridges left in the template will not be accepted in the refinement. Both the horizontal boundary and the highly tilted slope of the channel can be extracted.

Fig. 9.
Fig. 9.

Schematic layout of the gauge blocks.

Fig. 10.
Fig. 10.

OCT image of the step height of the ceramic gauge blocks (left). Small crosses represent the extracted boundaries with sub-pixel precision (right). The laboratory OCT system is used.

Fig. 11.
Fig. 11.

Cross-sectional OCT image of an alumina stack with two embedded channels (upper) and the image processing result (lower). The images were captured with the 1.3 μm laboratory OCT system and the layers are segmented simultaneously.

Fig. 12.
Fig. 12.

Cross-sectional OCT images of a two-layer alumina stack with deformation and delamination, as obtained with the 1.3 μm laboratory OCT. A Original OCT image, B the extracted pixels describing each boundary, and C an enlargement of the boundary in the area marked in B. The layers are segmented simultaneously. The vertical bar represents optical distance.

Fig. 13.
Fig. 13.

Cross-sectional OCT image of a single alumina ceramic layer (upper) and the image processing result (lower). The images were captured with the 1.3 μm laboratory OCT system. The vertical bar represents the optical distance.

Fig. 14.
Fig. 14.

A Original OCT image and B the extracted boundaries and the embedded channel. C Enlarged graph of the marked area in B. The image was obtained with the Thorlabs OCT and the layers are segmented simultaneously. The vertical bar represents optical distance and the horizontal bar represents geometrical distance.

Tables (3)

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Table 1. Specifications of SS OCT Systems

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Table 2. Step Height of Ceramic Gauge Blocks

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Table 3. Measurement of Alumina Sample Stack

Equations (13)

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μx(i,j)=1Kx·Kz*z=mmx=nnImg(i+z,j+x),
Vxi,j(k)=1Kx*x=nnImg(i+km,j+x).
μxmax(i,j)=max(Vxi,j)
Lx(i,j)=argmax(Vxi,j)
Lx(i,j)=floor(Kz/2)
Sx(i,j)=μx(i,j)/μxmax(i,j),
Sz(i,j)=μz(i,j)/μzmax(i,j).
ridgesX(i,j)=1ifSx(i,j)>Tx;0ifSx(i,j)Tx,
ridgesZ(i,j)=1ifSz(i,j)>Tz;0ifSz(i,j)Tz.
gradX(i,j)=0.5×[Img(i,j+1)Img(i,j1)],
gradZ(i,j)=0.5×[Img(i+1,j)Img(i1,j)].
θ(i,j)=arctan[gradZ(i,j)/gradX(i,j)],
SXZ(i,j)=gradX(i,j)2+gradZ(i,j)2,

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