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Phase and Texture Characterizations of Scar Collagen Second-Harmonic Generation Images Varied with Scar Duration

Published online by Cambridge University Press:  03 June 2015

Guannan Chen ,
Yao Liu ,
Xiaoqin Zhu ,
Zufang Huang ,
Jianyong Cai ,
Rong Chen ,
Shuyuan Xiong  and
Haishan Zeng
Guannan Chen
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China Department of Network and Communication Engineering, Fujian Normal University, Fuzhou 350007, China Imaging Unit—Integrative Oncology Department, British Columbia Cancer Agency Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
Yao Liu
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China Department of Network and Communication Engineering, Fujian Normal University, Fuzhou 350007, China
Xiaoqin Zhu*
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China
Zufang Huang
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China
Jianyong Cai
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China Department of Network and Communication Engineering, Fujian Normal University, Fuzhou 350007, China
Rong Chen
Affiliation:
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007, PR China
Shuyuan Xiong
Affiliation:
Department of Plastic Surgery, Affiliated First Hospital Fujian Medical University, Fuzhou 350005, PR China
Haishan Zeng
Affiliation:
Imaging Unit—Integrative Oncology Department, British Columbia Cancer Agency Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
*
*Corresponding author.[email protected]
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Abstract

This work developed a phase congruency algorithm combined with texture analysis to quantitatively characterize collagen morphology in second-harmonic generation (SHG) images from human scars. The extracted phase and texture parameters of the SHG images quantified collagen directionality, homogeneity, and coarseness in scars and varied with scar duration. Phase parameters showed an increasing tendency of the mean of phase congruency with scar duration, indicating that collagen fibers are better oriented over time. Texture parameters calculated from local difference local binary pattern (LD-LBP) and Haar wavelet transform, demonstrated that the LD-LBP variance decreased and the energy of all subimages increased with scar duration. It implied that collagen has a more regular pattern and becomes coarser with scar duration. In addition, the random forest regression was used to predict scar duration, demonstrating reliable performance of the extracted phase and texture parameters in characterizing collagen morphology in scar SHG images. Results indicate that the extracted parameters using the proposed method can be used as quantitative indicators to monitor scar progression with time and can help understand the mechanism of scar progression.

Keywords

scar progressioncollagensecond harmonic generation (SHG) imagephase parametertexture parameter
Type
Biological Applications and Techniques
Information
Microscopy and Microanalysis , Volume 21 , Issue 4 , August 2015 , pp. 855 - 862
Copyright
© Microscopy Society of America 2015 

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Footnotes

†

These co-first authors contributed equally to the work.

References

Adur, J., Pelegati, V.B., de Thomaz, A.A., Baratti, M.O., Andrade, L.A., Carvalho, H.F. & Cesar, C.L. (2014). Second harmonic generation microscopy as a powerful diagnostic imaging modality for human ovarian cancer. J Biophotonics 7, 37–48.CrossRefGoogle ScholarPubMed
Alvarez, M., Pina, D.R., Miranda, J.R.A. & Duarte, S.B. (2012). Application of wavelets to the evaluation of phantom images for mammography quality control. Phys Med Biol 57, 7177.CrossRefGoogle Scholar
Ambekar, R., Lau, T.Y., Walsh, M., Bhargava, R. & Toussaint, K.C. (2012). Quantifying collagen structure in breast biopsies using second-harmonic generation imaging. Biomed Opt Express 3, 2021–2035.CrossRefGoogle ScholarPubMed
Bredfeldt, J.S., Liu, Y., Pehlke, C.A., Conklin, M.W., Szulczewski, J.M., Inman, D.R. & Eliceiri, K.W. (2014). Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer. J Biomed Opt 19, 016007–016007.CrossRefGoogle ScholarPubMed
Breiman, L. (2001). Random forests. Mach Learn 45, 5–32.Google Scholar
Celik, T. & Tjahjadi, T. (2009). Multiscale texture classification using dual-tree complex wavelet transform. Pattern Recogn Lett 30, 331–339.Google Scholar
Cheng, W., Yan-hua, R., Fang-gang, N. & Guo-an, Z. (2013). The content and ratio of type I and III collagen in skin differ with age and injury. Afr J Biotechnol 10, 2524–2529.Google Scholar
Chung, J.H., Seo, J.Y., Choi, H.R., Lee, M.K., Youn, C.S., Rhie, G.E. & Eun, H.C. (2001). Modulation of skin collagen metabolism in aged and photoaged human skin in vivo. J Invest Dermatol 117, 1218–1224.CrossRefGoogle ScholarPubMed
Hu, W., Li, H., Wang, C., Gou, S. & Fu, L. (2012). Characterization of collagen fibers by means of texture analysis of second harmonic generation images using orientation-dependent gray level co-occurrence matrix method. J Biomed Opt 17, 0260071–0260079.Google Scholar
Kaatz, M., Sturm, A., Elsner, P., König, K., Bückle, R. & Koehler, M.J. (2010). Depth-resolved measurement of the dermal matrix composition by multiphoton laser tomography. Skin Res Technol 16, 131–136.Google Scholar
Kovesi, P. (1999). Image features from phase congruency. Videre J Comput Vis Res 1, 1–26.Google Scholar
Krishnan, M.M., Acharya, U.R., Chakraborty, C. & Ray, A.K. (2011). Automated diagnosis of oral cancer using higher order spectra features and local binary pattern: A comparative study. Technol Cancer Res T 10, 443–455.Google Scholar
Lau, T.Y., Sangha, H.K., Chien, E.K., McFarlin, B.L., Wagoner Johnson, A.J. & Toussaint, K.C. (2013). Application of Fourier transform-second-harmonic generation imaging to the rat cervix. J Microsc 251, 77–83.CrossRefGoogle Scholar
Liu, Y.Y., Chen, M., Ishikawa, H., Wollstein, G., Schuman, J.S. & Rehg, J.M. (2011). Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med Image Anal 15, 748–759.Google Scholar
Liu, Z. & Laganière, R. (2007). Phase congruence measurement for image similarity assessment. Pattern Recogn Lett 28, 166–172.Google Scholar
Mauri, A., Perrini, M., Mateos, J.M., Maake, C., Ochsenbein-Koelble, N., Zimmermann, R. & Mazza, E. (2013). Second harmonic generation microscopy of fetal membranes under deformation: Normal and altered morphology. Placenta 34, 1020–1026.CrossRefGoogle ScholarPubMed
Niessen, F.B., Spauwen, P.H., Schalkwijk, J. & Kon, M. (1999). On the nature of hypertrophic scars and keloids: A review. Plast Reconstr Surg 104, 1435–1458.Google Scholar
Ou, X., Pan, W. & Xiao, P. (2014). In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int J Pharm 460, 28–32.CrossRefGoogle ScholarPubMed
Pang, H., Lin, A., Holford, M., Enerson, B.E., Lu, B., Lawton, M.P. & Zhao, H. (2006). Pathway analysis using random forests classification and regression. Bioinformatics 22, 2028–2036.CrossRefGoogle ScholarPubMed
Park, S., Kim, B., Lee, J., Goo, J.M. & Shin, Y.G. (2011). GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis. IEEE Trans Biomed Eng 58, 2885–2894.Google Scholar
Profyris, C., Tziotzios, C. & Do Vale, I. (2012). Cutaneous scarring: Pathophysiology, molecular mechanisms, and scar reduction therapeutics: Part I. The molecular basis of scar formation. J Am Acad Dermatol 66, 1–10.CrossRefGoogle ScholarPubMed
Quellec, G., Lamard, M. & Josselin, P.M. (2008). Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imag 27, 1230–1241.CrossRefGoogle ScholarPubMed
Slemp, A.E. & Kirschner, R.E. (2006). Keloids and scars: A review of keloids and scars, their pathogenesis, risk factors, and management. Curr Opin Pediatr 18, 396–402.CrossRefGoogle ScholarPubMed
Subramani, P., Sahu, R. & Verma, S. (2006). Feature selection using Haar wavelet power spectrum. BMC Bioinformatics 7, 432.Google Scholar
Teng, S.W., Tan, H.Y., Sun, Y., Lin, S.J., Lo, W., Hsueh, C.M. & Dong, C.Y. (2007). Multiphoton fluorescence and second-harmonic-generation microscopy for imaging structural alterations in corneal scar tissue in penetrating full-thickness wound. Arch Ophthalmol 125, 977–978.Google Scholar
Theodossiou, T.A., Thrasivoulou, C., Ekwobi, C. & Becker, D.L. (2006). Second harmonic generation confocal microscopy of collagen type I from rat tendon cryosections. Biophys J 91, 4665–4677.Google Scholar
Tian, Y. (2011). Autofocus using image phase congruency. Opt Express 19, 261–270.CrossRefGoogle ScholarPubMed
Ud-Din, S., Volk, S. & Bayat, A. (2014). Regenerative healing, scar-free healing and scar formation across the species: Current concepts and future perspectives. Exp Dermatol 23, 615–619.CrossRefGoogle ScholarPubMed
Varani, J., Dame, M.K., Rittie, L., Fligiel, S.E., Kang, S., Fisher, G.J. & Voorhees, J.J. (2006). Decreased collagen production in chronologically aged skin: Roles of age-dependent alteration in fibroblast function and defective mechanical stimulation. Amer J Pathol 168, 1861–1868.Google Scholar
Verhaegen, P.D., Van Marle, J., Kuehne, A., Schouten, H.J., Gaffney, E.A., Maini, P.K. & Van Zuijlen, P.P. (2012). Collagen bundle morphometry in skin and scar tissue: A novel distance mapping method provides superior measurements compared to Fourier analysis. J Microsc 245, 82–89.Google Scholar
Wei, C.L., Raja, A.M., Xiao, G., Chang, S., Rajapakse, J.C., So, P.T. & Wee, A. (2009). Fibro-C-Index: Comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy. J Biomed Opt 14, 044013–044013.Google Scholar
Wen, B.L., Brewer, M.A., Nadiarnykh, O., Hocker, J., Singh, V., Mackie, T.R. & Campagnola, P.J. (2014). Texture analysis applied to second harmonic generation image data for ovarian cancer classification. J Biomed Opt 19, 096007.CrossRefGoogle ScholarPubMed
Wolfram, D., Tzankov, A., Pülzl, P. & Piza-Katzer, H. (2009). Hypertrophic scars and keloids-a review of their pathophysiology, risk factors, and therapeutic management. Dermatol Surg 35, 171–181.Google Scholar