Pearl Thickness Measurements from Optical Coherence Tomography Images

Abstract:

By measuring the thickness of the nacreous layer, pearl optical coherence tomography (OCT) images are now increasingly used in identification and characterization. An approach to automated thickness measurement of pearl OCT image has been reported in this paper. A Total Variational (TV) Model filter has been used to remove noise in images. After the image filtering, edges are detected by self-adaptive Canny operator. In the end we used circle fitting technologies to archive the goal of automated thickness measurement. The experimental results show this approach is accurate and adaptable.

• Periodical: Applied Mechanics and Materials (Volume 421)
• Pages: 415-420
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.421.415
• Online since: September 2013
• Authors: Jing Liu, Xiao Lin Tian, Yan Kui Sun
• Keywords: Canny Operator, Circle Fitting, Pearl OCT Image, Total Variational (TV) Model

References

[1] D. Huang, E. A. Swanson, C. P. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and et. al., Optical coherence tomography, Science 254(5035), 1178–1181 (1991).

DOI: 10.1126/science.1957169

[2] Nan Zeng, Yonghong He, Application of optical coherence tomography in nacre identification and characterization, MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Vol. 50, No. 2, February (2008).

DOI: 10.1002/mop.23124

[3] R. Y. Park, Y. C. Kim, and P. C. Kim, The comparative analyses of akoya pearls using the bio-bead nucleus and bead nucleus made from washboard shell, J. Korean Gems and Jewelry 1, 23–25 (2007).

[4] G. S. Zhang, X. D. Xie, S. Q. Qi, and P. C. Hu, X-ray diffraction study of nacre in shell of hyriopsis cumingii (LEA), J. Miner. Petrol. 22, 8–11 (2002).

[5] U. Wehrmeister, H. Goetz, D. E. Jacob, A. Soldati, W. Xu, H. Duschner, and W. Hofmeister, Visualization of the internal structures of cultured pearls by computerized X-ray microtomography, J. Geol. 31, 15–21 (2008).

DOI: 10.15506/jog.2008.31.1.15

[6] ZHANG Hong-ying, PENG Qi-cong. Adaptive image de noising model based on total variation[J]. Opto-Electronic Engineering, 2006, 33(3): 50-53.

[7] Rudin L 1，Osher S，Fatemi E Nonlinear total variation noise removal algorithms[J]．Physiea D·1992, 6：259-268.

DOI: 10.1016/0167-2789(92)90242-f

[8] Auber G，VeseLA variantal method in image recovery[J]．SlAM Journal of Numerical Analysis, 1997, 34(5)：1948-(1954).

[9] ZHANG Hong-ying, PENG Qi-cong. Adaptive image de noising model based on total variation[J]. Opto-Electronic Engineering, 2006, 33(3): 50-53.

[10] Liu Xi'ang, Xia Jinsong, Yang Donghe, Liu Yingchun. Cocoon edge detection based on self-adaptive Canny operator. Computer Science and Software Engineering, 2008 International Conference on, 12-14 Dec. (2008).

DOI: 10.1109/csse.2008.1046

[11] Andrew Fitsgibbon, Narizio Pilu, Robert B Fisher, Direct least square fitting of ellipse", John, "A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5), pp.476-480.

DOI: 10.1109/34.765658

[12] Canny J F. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8 (6): 679-698.

DOI: 10.1109/tpami.1986.4767851