Abstract

We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95.4% sensitivity and specificity, validated by leave-one-patient-out cross validation (LOPOCV), in detecting BCC in human skin samples collected from 42 patients. Moreover, we show the superiority of our classifier over the best possible classifier based on features extracted from intensity-only data, which demonstrates the significance of PR data in detecting BCC.

© 2016 Optical Society of America

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  1. R. S. Stern, “Prevalence of a history of skin cancer in 2007: Results of an incidence-based model,” Arch. Dermatol. 146, 279–282 (2010).
    [Crossref] [PubMed]
  2. UCSF School of Medicine, “Nonmelanoma skin cancer vs. melanoma,” http://dermatology.medschool.ucsf.edu/skincancer/general/MelanomavNon.aspx .
  3. M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
    [Crossref] [PubMed]
  4. D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
    [Crossref]
  5. D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
    [Crossref] [PubMed]
  6. A. F. Fercher, “Optical coherence tomography,” J. Biomed. Opt. 1, 157–173 (1996).
    [Crossref] [PubMed]
  7. R. Leitgeb, C. Hitzenberger, and A. F. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express 11, 889–894 (2003).
    [Crossref] [PubMed]
  8. T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
    [Crossref] [PubMed]
  9. M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
    [Crossref]
  10. O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
    [PubMed]
  11. M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).
  12. T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
    [Crossref]
  13. S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
    [Crossref] [PubMed]
  14. W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
    [Crossref]
  15. J. F. De Boer, T. E. Milner, M. J. van Gemert, and J. S. Nelson, “Two-dimensional birefringence imaging in biological tissue by polarization-sensitive optical coherence tomography,” Opt. Lett. 22, 934–936 (1997).
    [Crossref] [PubMed]
  16. C. K. Hitzenberger, E. Götzinger, M. Sticker, M. Pircher, and A. F. Fercher, “Measurement and imaging of birefringence and optic axis orientation by phase resolved polarization sensitive optical coherence tomography,” Opt. Express 9, 780–790 (2001).
    [Crossref] [PubMed]
  17. J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
    [Crossref] [PubMed]
  18. L. Duan, T. Marvdashti, A. Lee, J. Y. Tang, and A. K. Ellerbee, “Automated identification of basal cell carcinoma by polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 5, 3717 (2014).
    [Crossref] [PubMed]
  19. W. Trasischker, S. Zotter, T. Torzicky, B. Baumann, R. Haindl, M. Pircher, and C. K. Hitzenberger, “Single input state polarization sensitive swept source optical coherence tomography based on an all single mode fiber interferometer,” Biomed. Opt. Express 5, 2798–2809 (2014).
    [Crossref] [PubMed]
  20. K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
    [Crossref] [PubMed]
  21. C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
    [Crossref] [PubMed]
  22. A. Miyazawa, M. Yamanari, S. Makita, M. Miura, K. Kawana, K. Iwaya, H. Goto, and Y. Yasuno, “Tissue discrimination in anterior eye using three optical parameters obtained by polarization sensitive optical coherence tomography,” Opt. Express 17, 17426–17440 (2009).
    [Crossref] [PubMed]
  23. X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
    [Crossref]
  24. Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
    [Crossref] [PubMed]
  25. Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
    [Crossref] [PubMed]
  26. B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
    [Crossref] [PubMed]
  27. P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
    [Crossref] [PubMed]
  28. K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
    [Crossref] [PubMed]
  29. X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
    [Crossref]
  30. P. B. Garcia-Allende, I. Amygdalos, H. Dhanapala, R. D. Goldin, G. B. Hanna, and D. S. Elson, “Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues,” Biomed. Opt. Express 2, 2821–2836 (2011).
    [Crossref] [PubMed]
  31. J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).
  32. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
    [Crossref] [PubMed]
  33. J. Kittler, “Feature selection and extraction,” in Handbook of Pattern Recognition and Image Processing pp. 59–83 (1986).
  34. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of Ijcai14, 1137–1145 (1995).

2016 (2)

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

2015 (2)

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

2014 (4)

L. Duan, T. Marvdashti, A. Lee, J. Y. Tang, and A. K. Ellerbee, “Automated identification of basal cell carcinoma by polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 5, 3717 (2014).
[Crossref] [PubMed]

W. Trasischker, S. Zotter, T. Torzicky, B. Baumann, R. Haindl, M. Pircher, and C. K. Hitzenberger, “Single input state polarization sensitive swept source optical coherence tomography based on an all single mode fiber interferometer,” Biomed. Opt. Express 5, 2798–2809 (2014).
[Crossref] [PubMed]

K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
[Crossref] [PubMed]

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

2013 (1)

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

2012 (2)

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

2011 (2)

P. B. Garcia-Allende, I. Amygdalos, H. Dhanapala, R. D. Goldin, G. B. Hanna, and D. S. Elson, “Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues,” Biomed. Opt. Express 2, 2821–2836 (2011).
[Crossref] [PubMed]

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

2010 (2)

2009 (1)

2008 (2)

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

2007 (1)

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

2006 (1)

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

2005 (1)

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
[Crossref] [PubMed]

2004 (1)

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

2003 (2)

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

R. Leitgeb, C. Hitzenberger, and A. F. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express 11, 889–894 (2003).
[Crossref] [PubMed]

2001 (2)

C. K. Hitzenberger, E. Götzinger, M. Sticker, M. Pircher, and A. F. Fercher, “Measurement and imaging of birefringence and optic axis orientation by phase resolved polarization sensitive optical coherence tomography,” Opt. Express 9, 780–790 (2001).
[Crossref] [PubMed]

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

1997 (1)

1996 (1)

A. F. Fercher, “Optical coherence tomography,” J. Biomed. Opt. 1, 157–173 (1996).
[Crossref] [PubMed]

1991 (1)

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

1979 (1)

J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).

Alapati, U.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Alarcon, I.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Altmeyer, P.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Amir, S. B.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

Amygdalos, I.

Angst, R.

K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
[Crossref] [PubMed]

Applegate, B. E.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Artemyev, D. N.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Barton, J. K.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

Baumann, B.

Bechara, F. G.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Bieber, A. K.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Bienenfeld, A.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Bjerring, P.

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

Boone, M. A.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Brandon, J.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Bratchenko, I. A.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Brewer, M.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Chang, W.

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

Cheng, Y.-S.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Coleman, A.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

Cunha, D.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

de Boer, J. F.

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

J. F. De Boer, T. E. Milner, M. J. van Gemert, and J. S. Nelson, “Two-dimensional birefringence imaging in biological tissue by polarization-sensitive optical coherence tomography,” Opt. Lett. 22, 934–936 (1997).
[Crossref] [PubMed]

Del Marmol, V.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Dhanapala, H.

Ding, C.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
[Crossref] [PubMed]

Duan, L.

Ellerbee, A. K.

L. Duan, T. Marvdashti, A. Lee, J. Y. Tang, and A. K. Ellerbee, “Automated identification of basal cell carcinoma by polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 5, 3717 (2014).
[Crossref] [PubMed]

K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
[Crossref] [PubMed]

Ellis, J.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Elson, D. S.

Feldman, E.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Fercher, A. F.

Flotte, T.

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

Gambichler, T.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Gan, Y.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

Gao, W.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Garcia-Allende, P. B.

Gimenez-Conti, I.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Goldin, R. D.

Gossage, K. W.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

Goto, H.

Götzinger, E.

Gregory, K.

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

Haindl, R.

Hanna, G. B.

Hartigan, J. A.

J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).

Hee, M.

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

Hendon, C. P.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

Hitzenberger, C.

Hitzenberger, C. K.

Hoffmann, K.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Hofmann-Wellenhof, R.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Huang, D.

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

Isenberg, G.

X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
[Crossref]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

Iwaya, K.

Jemec, G. B.

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Jemec, G. B. E.

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

Jo, J. a.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Jorgensen, T. M.

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

Kaestle, R.

S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
[Crossref] [PubMed]

Kawana, K.

Kittler, J.

J. Kittler, “Feature selection and extraction,” in Handbook of Pattern Recognition and Image Processing pp. 59–83 (1986).

Kohavi, R.

R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of Ijcai14, 1137–1145 (1995).

Kornilin, D. V.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Lebwohl, M.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Lee, A.

Leitgeb, R.

Lin, C.

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

Lingley-Papadopoulos, C. A.

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

Loew, M. H.

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

Long, F.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
[Crossref] [PubMed]

Lurie, K. L.

K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
[Crossref] [PubMed]

Makita, S.

Mallipeddi, R.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

Malvehy, J.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Manyak, M. J.

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

Marboe, C. C.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

Markowitz, O.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Marneffe, A.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Marvdashti, T.

Milner, T. E.

Miura, M.

Miyamoto, M.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Miyazawa, A.

Mogensen, M.

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

Moussa, G.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Myakinin, O. O.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Neel, V.

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

Nelson, J. S.

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

J. F. De Boer, T. E. Milner, M. J. van Gemert, and J. S. Nelson, “Two-dimensional birefringence imaging in biological tissue by polarization-sensitive optical coherence tomography,” Opt. Lett. 22, 934–936 (1997).
[Crossref] [PubMed]

Norrenberg, S.

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Orchard, G.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

Orlikov, A.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Pan, Y.

Pande, P.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Park, B.

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

Park, B. H.

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

Park, J.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Pellacani, G.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Peng, H.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
[Crossref] [PubMed]

Pierce, M.

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

Pircher, M.

Puliafito, C.

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

Qi, X.

X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
[Crossref]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

Richardson, T.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

Rodriguez, J. J.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

Rollins, A. M.

X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
[Crossref]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

Sanders, M.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Sattler, E. C.

S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
[Crossref] [PubMed]

Saxer, C.

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

Schuh, S.

S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
[Crossref] [PubMed]

Schuman, J.

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

Schwartz, M.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Serafino, M. J.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Sheth, N.

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

Shrestha, S.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Siegel, D. M.

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

Sivak, M. V.

X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
[Crossref]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

Srinivas, S. M.

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

Stern, R. S.

R. S. Stern, “Prevalence of a history of skin cancer in 2007: Results of an incidence-based model,” Arch. Dermatol. 146, 279–282 (2010).
[Crossref] [PubMed]

Sticker, M.

Stinson, W.

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

Strasswimmer, J.

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

Stücker, M.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Suppa, M.

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

Swanson, E.

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

Tang, J. Y.

Tkaczyk, T. S.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

Torzicky, T.

Trasischker, W.

Tsay, D.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

Tycho, A.

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

van Gemert, M. J.

Vasa, R.

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

Wang, T.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Wang, X.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Welzel, J.

S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
[Crossref] [PubMed]

Willis, J. E.

X. Qi, Y. Pan, M. V. Sivak, J. E. Willis, G. Isenberg, and A. M. Rollins, “Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” Biomed. Opt. Express 1, 825–847 (2010).
[Crossref]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

Wong, M. A.

J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).

Yamanari, M.

Yang, Y.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Yasuno, Y.

Zakharov, V. P.

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

Zara, J. M.

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

Zhu, Q.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Zotter, S.

Arch. Dermatol. (1)

R. S. Stern, “Prevalence of a history of skin cancer in 2007: Results of an incidence-based model,” Arch. Dermatol. 146, 279–282 (2010).
[Crossref] [PubMed]

Biomed Opt. Express (1)

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed Opt. Express 3, 1548–1556 (2012).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

Brit. J. Dermatol. (2)

D. Cunha, T. Richardson, N. Sheth, G. Orchard, A. Coleman, and R. Mallipeddi, “Comparison of ex vivo optical coherence tomography with conventional frozen-section histology for visualizing basal cell carcinoma during Mohs micrographic surgery,” Brit. J. Dermatol. 165, 576–580 (2011).
[Crossref]

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging of basal cell carcinoma by high-definition optical coherence tomography: histomorphological correlation. A pilot study,” Brit. J. Dermatol. 167, 856–864 (2012).
[Crossref]

Exp. Dermatol. (1)

M. A. Boone, S. Norrenberg, G. B. Jemec, and V. Del Marmol, “Imaging actinic keratosis by high-definition optical coherence tomography. Histomorphologic correlation: a pilot study,” Exp. Dermatol. 22, 93–97 (2013).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

K. L. Lurie, R. Angst, and A. K. Ellerbee, “Automated mosaicing of feature-poor optical coherence tomography volumes with an integrated white light imaging system,” IEEE Trans. Biomed. Eng. 61, 2141–2153 (2014).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005).
[Crossref] [PubMed]

J. Biomed. Opt. (8)

C. A. Lingley-Papadopoulos, M. H. Loew, M. J. Manyak, and J. M. Zara, “Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis,” J. Biomed. Opt. 13, 024003 (2008).
[Crossref] [PubMed]

J. Strasswimmer, M. Pierce, B. Park, and V. Neel, “Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma,” J. Biomed. Opt. 9, 292–298 (2004).
[Crossref] [PubMed]

A. F. Fercher, “Optical coherence tomography,” J. Biomed. Opt. 1, 157–173 (1996).
[Crossref] [PubMed]

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref] [PubMed]

B. H. Park, C. Saxer, S. M. Srinivas, J. S. Nelson, and J. F. de Boer, “In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography,” J. Biomed. Opt. 6, 474–479 (2001).
[Crossref] [PubMed]

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. Brandon, Y.-S. Cheng, B. E. Applegate, and J. a. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003).
[Crossref] [PubMed]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006).
[Crossref]

J. Clin. Aesthet. Dermatol. (1)

O. Markowitz, M. Schwartz, E. Feldman, A. Bienenfeld, A. K. Bieber, J. Ellis, U. Alapati, M. Lebwohl, and D. M. Siegel, “Evaluation of optical coherence tomography as a means of identifying earlier stage basal cell carcinomas while reducing the use of diagnostic biopsy,” J. Clin. Aesthet. Dermatol. 8, 14 (2015).
[PubMed]

J. Dermatol. Sci. (1)

T. Gambichler, A. Orlikov, R. Vasa, G. Moussa, K. Hoffmann, M. Stücker, P. Altmeyer, and F. G. Bechara, “In vivo optical coherence tomography of basal cell carcinoma,” J. Dermatol. Sci. 45, 167–173 (2007).
[Crossref] [PubMed]

J. Eur. Acad. Dermatol. Venereol. (1)

M. A. Boone, A. Marneffe, M. Suppa, M. Miyamoto, I. Alarcon, R. Hofmann-Wellenhof, J. Malvehy, G. Pellacani, and V. Del Marmol, “High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma,” J. Eur. Acad. Dermatol. Venereol. 29(8) 1–10 (2015).

J. Innov. Opt. Health Sci. (1)

W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, and D. V. Kornilin, “Medical images classification for skin cancer using quantitative image features with optical coherence tomography,” J. Innov. Opt. Health Sci. 9, 1650003 (2016).
[Crossref]

J. R. Stat. Soc. Ser. C Appl. Stat. (1)

J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).

Opt. Express (3)

Opt. Lett. (1)

Science (1)

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

Skin Res. Technol. (1)

T. M. Jorgensen, A. Tycho, M. Mogensen, P. Bjerring, and G. B. E. Jemec, “Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography,” Skin Res. Technol. 14, 364–369 (2008).
[Crossref]

Other (4)

S. Schuh, R. Kaestle, E. C. Sattler, and J. Welzel, “Optical coherence tomography of actinic keratoses and basal cell carcinomas - differentiation by quantification of signal intensity and layer thickness,” J. Eur. Acad. Dermatol. Venereol. (2016).
[Crossref] [PubMed]

UCSF School of Medicine, “Nonmelanoma skin cancer vs. melanoma,” http://dermatology.medschool.ucsf.edu/skincancer/general/MelanomavNon.aspx .

J. Kittler, “Feature selection and extraction,” in Handbook of Pattern Recognition and Image Processing pp. 59–83 (1986).

R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of Ijcai14, 1137–1145 (1995).

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Figures (7)

Fig. 1
Fig. 1 Representative intensity (left), PR (middle) and histology (right) images of healthy and BCC human skin. (a), (b) and (c) represent the images of a healthy skin. Normal skin appendages such as a hair follicle (HF, yellow arrow) and sebaceous glands (SG, light pink arrow) are noticeable. (d), (e), and (f) images represent the case of a nodular BCC, and (g), (h) and (i) images represent the case of infiltrative BCC. The white arrows point to nodular tumor islands. The scale bars represent 500 µm × 500 µm and are applicable to all images in a row.
Fig. 2
Fig. 2 Examples of (a) the extracted surface (serves as the top boundary) and the two types of ROI: (b) fixed ROI: 200 pixels (940 µm) beneath the surface, and (c) binary ROI: thresholding the intensity values (threshold value is calculated to be 100 dB here). The scale bars represent 500 µm × 500 µm.
Fig. 3
Fig. 3 Examples of filtered (a, and c) and down-sampled (b, and d) intensity and PR images, respectively. Down-sampled images are constructed by retaining every five Ascans (yellow or red colored Ascans).
Fig. 4
Fig. 4 Schematic illustration of the process for extracting Ascan-based features from a (a) down-sampled image: (b) construct the intermediate feature matrix by extracting several intermediate features from all Ascans (m=152), (c) calculate statistics (mean, std, min, and max) across the rows of intermediate feature matrix, and (d) construct final Ascan-based feature vector by reshaping the intermediate feature matrix into a vector.
Fig. 5
Fig. 5 Process for calculating all intermediate Ascan-based features from representative (a) intensity and (g) PR Ascans. In all images, dashed-red lines represent fitted lines, except in (h) where the dashed-red curve represents a fitted 5th-order polynomial. Vertical dashed-black lines represent the fitting ranges. Long-range intermediate features are calculated by fitting (b) a line to intensity and (h) a fifth-order polynomial to PR Ascans. Short-range intermediate features are calculated by fitting lines to data selected by an axially-moving window. The red rectangles represent the first window, and the window is translated axially (red arrows) in one-pixel steps. Peaks and valleys intermediate features are calculated from axially flattened (d) intensity and (j) PR Ascans. Blue and green lines represent peaks and valleys, respectively. Segment intermediate features are calculated by fitting lines to (e) intensity and (k) PR Ascans segments defined by local maxima (blue triangles) and local minima (inverted green triangles). Crossing intermediate features are calculated by counting the number of times (f) intensity and (l) PR Ascans cross predefined crossing levels (dashed horizontal lines). INT: intensity and Norm.: normalized.
Fig. 6
Fig. 6 Examples of four (a) intensity and (b) PR image regions calculated using k-means algorithm for morphological analysis. The scale bars represent 500 µm × 500 µm.
Fig. 7
Fig. 7 Accuracy, sensitivity, and specificity calculated at each iteration of the forward search process for a classifier (a) based on both intensity and PR features, and (b) intensity-only features using SVM with Gaussian kernels of σ = 4 and σ = 5 algorithms, respectively. Vertical dashed black line represents the most accurate classifier achieved using smallest number of features. (c) ROC for classifiers built from intensity-only, and both intensity and PR features.

Tables (3)

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Table 1 Number of PS-OCT images (B-scans) and the total number of patients per class.* Not a miscalculation.

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Table 2 Number of extracted features and type of ROI used for each feature category. Int: intensity, PR: phase retardation, F: fixed, and B: binary.

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Table 3 Number of final selected features for each feature category.

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