Abstract

Optical coherence tomography (OCT) has proven to be a useful tool for investigating internal structures in ceramic tapes, and the technique is expected to be important for roll-to-roll manufacturing. However, because of high scattering in ceramic materials, noise and speckles deteriorate the image quality, which makes automated quantitative measurements of internal interfaces difficult. To overcome this difficulty we present in this paper an innovative image analysis approach based on volumetric OCT data. The engine in the analysis is a 3D image processing and analysis algorithm. It is dedicated to boundary segmentation and dimensional measurement in volumetric OCT images, and offers high accuracy, efficiency, robustness, subpixel resolution, and a fully automated operation. The method relies on the correlation property of a physical interface and effectively eliminates pixels caused by noise and speckles. The remaining pixels being stored are the ones confirmed to be related to the target interfaces. Segmentation of tilted and curved internal interfaces separated by 10μm in the Z direction is demonstrated. The algorithm also extracts full-field top-view intensity maps of the target interfaces for high-accuracy measurements in the X and Y directions. The methodology developed here may also be adopted in other similar 3D imaging and measurement technologies, e.g., ultrasound imaging, and for various materials.

© 2014 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

Peter Ekberg, Rong Su, Ernest W. Chang, Seok Hyun Yun, and Lars Mattsson
J. Opt. Soc. Am. A 31(2) 217-226 (2014)

Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic

Rong Su, Mikhail Kirillin, Peter Ekberg, Arne Roos, Ekaterina Sergeeva, and Lars Mattsson
Opt. Express 20(4) 4603-4618 (2012)

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans

Zaixing Mao, Atsuya Miki, Song Mei, Ying Dong, Kazuichi Maruyama, Ryo Kawasaki, Shinichi Usui, Kenji Matsushita, Kohji Nishida, and Kinpui Chan
Biomed. Opt. Express 10(11) 5832-5851 (2019)

References

  • View by:
  • |
  • |
  • |

  1. W. Drexler and J. G. Fujimoto, eds., Optical Coherence Tomography Technology and Applications (Springer, 2008).
  2. D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007).
    [Crossref]
  3. 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]
  4. R. Su, M. Kirillin, E. W. Chang, E. Sergeeva, S. H. Yun, and L. Mattsson, “Perspectives of mid-infrared optical coherence tomography for inspection and micrometrology of industrial ceramics,” Opt. Express 22, 15804–15819 (2014).
    [Crossref]
  5. Multilayer, FP7 European project, http://multilayer.4m-association.org/ .
  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. M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16, 12313–12333 (2008).
    [Crossref]
  8. M. K. Garvin, M. D. Abràmoff, 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).
  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. Q. Li, M. L. Onozato, P. M. Andrews, C. Chen, A. Paek, R. Naphas, S. Yuan, J. Jiang, A. Cable, and Y. Chen, “Automated quantification of microstructural dimensions of the human kidney using optical coherence tomography (OCT),” Opt. Express 17, 16000–16016 (2009).
    [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. P. Ekberg, R. Su, E. W. Chang, S. H. Yun, and L. Mattsson, “Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data,” J. Opt. Soc. Am. A 31, 217–226 (2014).
    [Crossref]
  13. G. Dougherty, ed., Medical Image Processing: Techniques and Applications (Springer, 2011).
  14. Thorlabs Telesto 1325 nm OCT systems, http://www.thorlabs.de/newgrouppage9.cfm?objectgroup_id=5274 .
  15. Swerea IVF, Mölndal (head office, P. O. Box 104. SE-431 22 Mölndal. Sweden. http://swerea.se/en/ .
  16. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice Hall, 2008).
  17. Zygo NewView7300 3D optical surface profiler, http://www.zygo.com/?/met/profilers/newview7000/ .
  18. R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust. Speech Signal Process. 29, 1153–1160 (1981).
  19. 1951 USAF resolution test chart, http://en.wikipedia.org/wiki/1951_USAF_resolution_test_chart .
  20. S. K. Kachigan, Statistical Analysis (Radius, 1986).
  21. J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. XI.
  22. Amira, 3D Analysis Software for Life Sciences, http://www.vsg3d.com/amira/overview .

2014 (2)

2012 (1)

2010 (1)

2009 (3)

2008 (1)

2007 (1)

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

1999 (1)

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

1981 (1)

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust. Speech Signal Process. 29, 1153–1160 (1981).

Abràmoff, M. D.

M. K. Garvin, M. D. Abràmoff, 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).

Andrews, P. M.

Bizheva, K.

Burns, T. L.

M. K. Garvin, M. D. Abràmoff, 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).

Cable, A.

Chang, E. W.

Chen, C.

Chen, Y.

Clausi, D. A.

Creath, K.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. XI.

Drexler, W.

Ekberg, P.

Gargesha, M.

Garvin, M. K.

M. K. Garvin, M. D. Abràmoff, 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).

Gonzalez, R. C.

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

Hermann, B.

Hofer, B.

Jenkins, M. W.

Jiang, J.

Kachigan, S. K.

S. K. Kachigan, Statistical Analysis (Radius, 1986).

Kajic, V.

Keys, R.

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust. Speech Signal Process. 29, 1153–1160 (1981).

Kirillin, M.

Li, Q.

Marshall, D.

Mattsson, L.

Mishra, A.

Naphas, R.

Onozato, M. L.

Paek, A.

Považay, B.

Rollins, A. M.

Roos, A.

Rosin, P. L.

Russell, S. R.

M. K. Garvin, M. D. Abràmoff, 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).

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]

Sergeeva, E.

Sonka, M.

M. K. Garvin, M. D. Abràmoff, 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).

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.

Wilson, D. L.

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. Abràmoff, 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).

Wyant, J. C.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. XI.

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]

Yuan, S.

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 (1)

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

IEEE Trans. Acoust. Speech Signal Process. (1)

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust. Speech Signal Process. 29, 1153–1160 (1981).

IEEE Trans. Med. Imaging (1)

M. K. Garvin, M. D. Abràmoff, 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).

J. Biomed. Opt. (1)

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

J. Opt. Soc. Am. A (1)

Opt. Express (6)

Other (11)

1951 USAF resolution test chart, http://en.wikipedia.org/wiki/1951_USAF_resolution_test_chart .

S. K. Kachigan, Statistical Analysis (Radius, 1986).

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” in Applied Optics and Optical Engineering, R. R. Shannon and J. C. Wyant, eds. (Academic, 1992), Vol. XI.

Amira, 3D Analysis Software for Life Sciences, http://www.vsg3d.com/amira/overview .

W. Drexler and J. G. Fujimoto, eds., Optical Coherence Tomography Technology and Applications (Springer, 2008).

Multilayer, FP7 European project, http://multilayer.4m-association.org/ .

G. Dougherty, ed., Medical Image Processing: Techniques and Applications (Springer, 2011).

Thorlabs Telesto 1325 nm OCT systems, http://www.thorlabs.de/newgrouppage9.cfm?objectgroup_id=5274 .

Swerea IVF, Mölndal (head office, P. O. Box 104. SE-431 22 Mölndal. Sweden. http://swerea.se/en/ .

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

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

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (23)

Fig. 1.
Fig. 1. Schematic setup of a spectral domain OCT (SD-OCT).
Fig. 2.
Fig. 2. Geometric model of the ceramic sample stack for OCT imaging. The centers of the imaging sites are marked with the red frames.
Fig. 3.
Fig. 3. Example of a cross sectional OCT B-scan of the multilayered ceramic sample. The enlarged image of the boundary is shown on the left hand side and the corresponding average A-scan is shown on the right hand side.
Fig. 4.
Fig. 4. Steps of the ridge detection algorithm.
Fig. 5.
Fig. 5. Images describing the different steps in the ridge detection algorithm. (A) Input OCT image; (B) the logical connection map that contains the results after extracting the longest ridges in all directions; (C) the final logical template of ridges after the merging and cleaning process. The vertical bar in (A) represents optical distance.
Fig. 6.
Fig. 6. Eight pixel neighborhood of a ridge pixel, i, j (marked with a white square), belonging to a ridge in the X direction. The black arrows are the gradients vectors and the locations of the local maxima are marked with red dots. The red crosshair is the final subpixel location of the ridge pixel at location i, j.
Fig. 7.
Fig. 7. Result of the subpixel refinement based on the candidate pixels found in Fig. 5(C). The contrast of the image is enhanced for display reasons, where the gray scale corresponds to a dynamic range of 45 dB. The image processing is done on the original OCT data with a dynamic range of 110 dB.
Fig. 8.
Fig. 8. 3D perspective OCT image of the ceramic layer with laser-milled microchannels. The total depth of the image is 1.78 mm (optical distance). The image is rendered using the maximum intensity projection (MIP) method [13].
Fig. 9.
Fig. 9. Processing X-Z cross sectional OCT images in the volumetric data using the ridge detection algorithm.
Fig. 10.
Fig. 10. Extracted ridge pixels of the boundaries of the microchannels. Some noise pixels are also extracted due to the non-optimized image processing parameters.
Fig. 11.
Fig. 11. Processing Y-Z cross sectional OCT images in the volumetric data for further extraction of ridge pixels.
Fig. 12.
Fig. 12. Ridge detection results of the volumetric OCT data from (A) X-Z view and (B) Y-Z view. The results are shown in 3D perspectives, where the depth range of the green box is limited to 192.1–253.3 pixels, which corresponds to the optical depth of 667.8–880.6 μm. Thus, the curvature of the surface more pronounced in this case as compared with Fig. 8.
Fig. 13.
Fig. 13. Effect of merging the ridge detection results of the X-Z and Y-Z views with an overlap criteria of 0.1 pixel. The depth range of the green box is limited to 193.2–233.9 pixels, which corresponds to an optical depth of 671.7–813.2 μm.
Fig. 14.
Fig. 14. Segmentation of the (A) top and (B) bottom surfaces of the ceramic layer based on the result shown in Fig. 13. The polynomial fitting of the surfaces are plotted by false colors and the extracted ridge pixels are presented as small gray dots. The unit of the axes is in pixels.
Fig. 15.
Fig. 15. Top-view gray-scale images of the (A) top and (B) bottom surfaces of the ceramic layer derived from the 3D correlation algorithm. The gray levels cover a 110 dB dynamic range. The brighter pixels in both images correspond to the surfaces, while the dark pixels in the upper image correspond to the air and the dark gray pixels in the lower image originate from the backscattering signal within the ceramic material. The image size is 1024×512 pixels for 4 mm×4 mm (the obtained image is twice as wide as the sample actually is).
Fig. 16.
Fig. 16. Canny detection combined with the subpixel refinement algorithm for edge detection and subsequent measurement of the widths of the channels using the upper image of Fig. 15. The small red crosses present the result of the edge detection.
Fig. 17.
Fig. 17. Top-view gray-scale OCT image of the extracted top surface of the USAF 1951 resolution test target.
Fig. 18.
Fig. 18. Plot of the least square fit wavefront of an OCT system using a pair of galvo mirrors for the lateral scanning. The FOV is 4mm×4mm and the colorbar corresponds to height values in micrometers. The peak-to-valley (P-V) value of the deviation is around 20 μm.
Fig. 19.
Fig. 19. 3D perspective OCT image of the two-layer ceramic stack (render mode is MIP). The channel layer is embedded beneath a 375 μm thick zirconia layer. The image contains 1024×512×512 pixels that correspond to 4mm×4mm×1.78mm in X, Y and Z directions, respectively, where the Z axis represents optical distance.
Fig. 20.
Fig. 20. Cross sectional OCT image of the microchannels with a 375 μm thick zirconia top layer. The contrast of the image is enhanced for display reasons, where the gray scale represents a 45 dB dynamic range. The vertical bar presents optical distance.
Fig. 21.
Fig. 21. Optimized histogram for fine segmentation using a 0.1 pixel resolution of the original OCT z pixels. Two peaks are found after removing the form. They represent the lower surface of the top layer and the top surface of the channel. One z pixel in the right hand histogram corresponds to 0.348 μm.
Fig. 22.
Fig. 22. Segmentation of the surfaces of the ceramic stack from the OCT data in Fig. 19. Three extracted surfaces are shown: (A) the bottom surface of the zirconia top layer, and (B) the top and (C) bottom surfaces of the channel structure. The polynomial fittings of the surfaces are plotted by false colors and the extracted ridge pixels are presented as small gray dots. The unit of the axes is in pixels.
Fig. 23.
Fig. 23. Comparison of top-view OCT images of the top and bottom surfaces of the microchannels that are extracted by (A) the 3D image processing algorithm and (B) the manual selection of cross sectional image using conventional software, e.g., Amira [22].

Tables (2)

Tables Icon

Table 1. Specification of the OCT System

Tables Icon

Table 2. Line Widths in the USAF 1951 Resolution Test Target

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

Md=MXZ·MYZ,
M01=Ms1·M0L,
M02=Ms2·M0L.

Metrics