Abstract

Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2015 (4)

A. Mamalis, D. Ho, and J. Jagdeo, “Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: A systematic review,” Dermatol. Surg. 41, 993–1005 (2015).
[PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

D. Kaba, Y. Wang, C. Wang, X. Liu, H. Zhu, A. Salazar-Gonzalez, and Y. Li, “Retina layer segmentation using kernel graph cuts and continuous max-flow,” Opt. Express 23, 7366–7384 (2015).
[Crossref] [PubMed]

2013 (3)

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

E. Sattler, R. Kästle, and J. Welzel, “Optical coherence tomography in dermatology,” J. Biomed. Opt. 18, 061224 (2013).
[Crossref] [PubMed]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

2012 (2)

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 9, 671–675 (2012).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

2011 (2)

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

T. Gambichler, V. Jaedicke, and S. Terras, “Optical coherence tomography in dermatology: technical and clinical aspects,” Arch. Dermatol. Res. 303, 457–473 (2011).
[Crossref] [PubMed]

2010 (1)

2009 (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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

2008 (1)

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

2006 (3)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Y. Hori, Y. Yasuno, S. Sakai, M. Matsumoto, T. Sugawara, V. Madjarova, M. Yamanari, S. Makita, T. Yasui, T. Araki, and M. Itoh, “Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography,” Opt. Express 14, 1862–1877 (2006).
[Crossref] [PubMed]

2004 (2)

J. Weissman, T. Hancewicz, and P. Kaplan, “Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis,” Opt. Express 12, 5760–5769 (2004).
[Crossref] [PubMed]

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26, 1124–1137 (2004).
[Crossref]

2003 (1)

J. Sandby-Moller, T. Poulsen, and H. C. Wulf, “Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits,” Acta Derm-Venereol 83, 410–413 (2003).
[Crossref] [PubMed]

2001 (1)

T. W. Fischer, W. Wigger-Alberti, and P. Elsner, “Assessment of ‘dry skin’: Current bioengineering methods and test designs,” Skin Pharmacol. Physi. 14, 183–195 (2001).
[Crossref]

Abramoff, M. D.

K. Lee, L. Zhang, M. D. Abramoff, and M. Sonka, “Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2015), pp. 94133X.

Abràmoff, M. D.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

Altmeyer, P.

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Amzica, F.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Antony, B. J.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

Araki, T.

Badea, R.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Bai, J.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

Batatia, H.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

Berardesca, E.

K.-P. Wilhelm, P. Elsner, E. Berardesca, and H. I. Maibach, “Bioengineering of the skin: skin imaging & analysis,” (2006).

Bloemen, M. C.

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

Blume-Peytavi, U.

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

Boykov, Y.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26, 1124–1137 (2004).
[Crossref]

Buatti, J. M.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

Chen, D. Z.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

Chen, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Chen, X.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Cheng, J.

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

Chiu, S. J.

Crisan, D.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Crisan, M.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Dobos, G.

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

Eliceiri, K. W.

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 9, 671–675 (2012).
[Crossref] [PubMed]

Elsner, P.

T. W. Fischer, W. Wigger-Alberti, and P. Elsner, “Assessment of ‘dry skin’: Current bioengineering methods and test designs,” Skin Pharmacol. Physi. 14, 183–195 (2001).
[Crossref]

K.-P. Wilhelm, P. Elsner, E. Berardesca, and H. I. Maibach, “Bioengineering of the skin: skin imaging & analysis,” (2006).

Farsiu, S.

Fischer, T. W.

T. W. Fischer, W. Wigger-Alberti, and P. Elsner, “Assessment of ‘dry skin’: Current bioengineering methods and test designs,” Skin Pharmacol. Physi. 14, 183–195 (2001).
[Crossref]

Frangi, A. F.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” MICCAI,” (1998).

Gambichler, T.

T. Gambichler, V. Jaedicke, and S. Terras, “Optical coherence tomography in dermatology: technical and clinical aspects,” Arch. Dermatol. Res. 303, 457–473 (2011).
[Crossref] [PubMed]

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Gao, E.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Garvin, M. K.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

George, J.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

Haeker, M.

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

Hancewicz, T.

Ho, D.

A. Mamalis, D. Ho, and J. Jagdeo, “Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: A systematic review,” Dermatol. Surg. 41, 993–1005 (2015).
[PubMed]

Hoffmann, K.

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Hori, Y.

Itoh, M.

Izatt, J. A.

Jaedicke, V.

T. Gambichler, V. Jaedicke, and S. Terras, “Optical coherence tomography in dermatology: technical and clinical aspects,” Arch. Dermatol. Res. 303, 457–473 (2011).
[Crossref] [PubMed]

Jagdeo, J.

A. Mamalis, D. Ho, and J. Jagdeo, “Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: A systematic review,” Dermatol. Surg. 41, 993–1005 (2015).
[PubMed]

Kaba, D.

Kaplan, P.

Kardon, R.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

Kästle, R.

E. Sattler, R. Kästle, and J. Welzel, “Optical coherence tomography in dermatology,” J. Biomed. Opt. 18, 061224 (2013).
[Crossref] [PubMed]

Kolmogorov, V.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26, 1124–1137 (2004).
[Crossref]

Kottner, J.

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

Kwon, Y. H.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

Lagarde, J.-M.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

Lee, K.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

K. Lee, L. Zhang, M. D. Abramoff, and M. Sonka, “Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2015), pp. 94133X.

Li, A.

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

Li, X. T.

Li, Y.

Liu, J.

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

Liu, X.

Lupsor, M.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Madjarova, V.

Maibach, H. I.

K.-P. Wilhelm, P. Elsner, E. Berardesca, and H. I. Maibach, “Bioengineering of the skin: skin imaging & analysis,” (2006).

Makita, S.

Mamalis, A.

A. Mamalis, D. Ho, and J. Jagdeo, “Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: A systematic review,” Dermatol. Surg. 41, 993–1005 (2015).
[PubMed]

Matip, R.

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Matsumoto, M.

Mcheik, A.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

Middelkoop, E.

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

Moussa, G.

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Nicholas, P.

Niemeijer, M.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Niessen, W. J.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” MICCAI,” (1998).

Poulsen, T.

J. Sandby-Moller, T. Poulsen, and H. C. Wulf, “Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits,” Acta Derm-Venereol 83, 410–413 (2003).
[Crossref] [PubMed]

Rasband, W. S.

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 9, 671–675 (2012).
[Crossref] [PubMed]

Richter, C.

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

Sakai, S.

Salazar-Gonzalez, A.

Sandby-Moller, J.

J. Sandby-Moller, T. Poulsen, and H. C. Wulf, “Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits,” Acta Derm-Venereol 83, 410–413 (2003).
[Crossref] [PubMed]

Sannino, G.

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

Sattler, E.

E. Sattler, R. Kästle, and J. Welzel, “Optical coherence tomography in dermatology,” J. Biomed. Opt. 18, 061224 (2013).
[Crossref] [PubMed]

Scharr, H.

H. Scharr, “Optimal operators in digital image processing,” Ph.D. thesis (2000).

Schneider, C. A.

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 9, 671–675 (2012).
[Crossref] [PubMed]

Shah, V. A.

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

Shi, F.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Smith, L. I.

L. I. Smith, “A tutorial on principal components analysis,” (2002).

Song, Q.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

Sonka, M.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

K. Lee, L. Zhang, M. D. Abramoff, and M. Sonka, “Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2015), pp. 94133X.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

Srivastava, R.

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

Sugawara, T.

Tauber, C.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

Terras, S.

T. Gambichler, V. Jaedicke, and S. Terras, “Optical coherence tomography in dermatology: technical and clinical aspects,” Arch. Dermatol. Res. 303, 457–473 (2011).
[Crossref] [PubMed]

Tey, H.

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

Tey, H. L.

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

Toth, C. A.

Trojahn, C.

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

van der Wal, M. B.

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

van Gerven, M. S.

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

Verhaegen, P. D.

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

Viergever, M. A.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” MICCAI,” (1998).

Vincken, K. L.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” MICCAI,” (1998).

Wall, C.

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

Wang, C.

Wang, Y.

Weissman, J.

Welzel, J.

E. Sattler, R. Kästle, and J. Welzel, “Optical coherence tomography in dermatology,” J. Biomed. Opt. 18, 061224 (2013).
[Crossref] [PubMed]

Wigger-Alberti, W.

T. W. Fischer, W. Wigger-Alberti, and P. Elsner, “Assessment of ‘dry skin’: Current bioengineering methods and test designs,” Skin Pharmacol. Physi. 14, 183–195 (2001).
[Crossref]

Wilhelm, K.-P.

K.-P. Wilhelm, P. Elsner, E. Berardesca, and H. I. Maibach, “Bioengineering of the skin: skin imaging & analysis,” (2006).

Wong, D.

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

Wong, D. W. K.

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

Wu, X.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

Wulf, H. C.

J. Sandby-Moller, T. Poulsen, and H. C. Wulf, “Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits,” Acta Derm-Venereol 83, 410–413 (2003).
[Crossref] [PubMed]

Xiang, D.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Yamanari, M.

Yasui, T.

Yasuno, Y.

Yow, A. P.

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

Zhang, L.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

K. Lee, L. Zhang, M. D. Abramoff, and M. Sonka, “Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2015), pp. 94133X.

Zhao, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Zhu, H.

Zhu, W.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

Acta Derm-Venereol (1)

J. Sandby-Moller, T. Poulsen, and H. C. Wulf, “Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits,” Acta Derm-Venereol 83, 410–413 (2003).
[Crossref] [PubMed]

Arch. Dermatol. Res. (2)

M. Crisan, D. Crisan, G. Sannino, M. Lupsor, R. Badea, and F. Amzica, “Ultrasonographic staging of cutaneous malignant tumors: an ultrasonographic depth index,” Arch. Dermatol. Res. 305, 305–313 (2013).
[Crossref] [PubMed]

T. Gambichler, V. Jaedicke, and S. Terras, “Optical coherence tomography in dermatology: technical and clinical aspects,” Arch. Dermatol. Res. 303, 457–473 (2011).
[Crossref] [PubMed]

Dermatol. Surg. (1)

A. Mamalis, D. Ho, and J. Jagdeo, “Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: A systematic review,” Dermatol. Surg. 41, 993–1005 (2015).
[PubMed]

IEEE T. Med. Imaging (5)

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE T. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

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 T. Med. Imaging 28, 1436–1447 (2009).
[Crossref]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE T. Med. Imaging 34, 441–452 (2015).
[Crossref]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut,” IEEE T. Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE T. Med. Imaging 32, 376–386 (2013).
[Crossref]

IEEE T. Pattern Anal. (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE T. Pattern Anal. 28, 119–134 (2006).
[Crossref]

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26, 1124–1137 (2004).
[Crossref]

J. Am. Acad. Dermatol. (1)

M. C. Bloemen, M. S. van Gerven, M. B. van der Wal, P. D. Verhaegen, and E. Middelkoop, “An objective device for measuring surface roughness of skin and scars,” J. Am. Acad. Dermatol. 64, 706–715 (2011).
[Crossref] [PubMed]

J. Biomed. Opt. (2)

C. Trojahn, G. Dobos, C. Richter, U. Blume-Peytavi, and J. Kottner, “Measuring skin aging using optical coherence tomography in vivo: a validation study,” J. Biomed. Opt. 20, 045003 (2015).
[Crossref] [PubMed]

E. Sattler, R. Kästle, and J. Welzel, “Optical coherence tomography in dermatology,” J. Biomed. Opt. 18, 061224 (2013).
[Crossref] [PubMed]

J. Dermatol. Sci. (1)

T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44, 145–152 (2006).
[Crossref] [PubMed]

Nat. Methods (1)

C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nat. Methods 9, 671–675 (2012).
[Crossref] [PubMed]

Opt. Express (4)

Skin Pharmacol. Physi. (1)

T. W. Fischer, W. Wigger-Alberti, and P. Elsner, “Assessment of ‘dry skin’: Current bioengineering methods and test designs,” Skin Pharmacol. Physi. 14, 183–195 (2001).
[Crossref]

Other (11)

A. Li, J. Cheng, A. P. Yow, C. Wall, D. Wong, H. Tey, and J. Liu, “Epidermal segmentation in high-definition optical coherence tomography,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 2015, 3045(2015).

A. P. Yow, J. Cheng, A. Li, R. Srivastava, J. Liu, D. W. K. Wong, and H. L. Tey, “Automated in vivo 3D high-definition optical coherence tomography skin analysis system,” Conf. Proc. IEEE Eng. Med. Biol. Soc.2016, 3895–3898 (2016).

K.-P. Wilhelm, P. Elsner, E. Berardesca, and H. I. Maibach, “Bioengineering of the skin: skin imaging & analysis,” (2006).

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” MICCAI,” (1998).

L. I. Smith, “A tutorial on principal components analysis,” (2002).

“Agfa-healthcare,” Last accessed 08/09/17.

H. Scharr, “Optimal operators in digital image processing,” Ph.D. thesis (2000).

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M. D. Abràmoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” in “Med. Imaging,” (International Society for Optics and Photonics, 2007), pp. 651214.

A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde, “Speckle modelization in OCT images for skin layers segmentation,” in “VISAPP, volume 1 (2008), pp. 347–350.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2012), pp. 83141G.

K. Lee, L. Zhang, M. D. Abramoff, and M. Sonka, “Fast and memory-efficient LOGISMOS graph search for intraretinal layer segmentation of 3D macular OCT scans,” in “SPIE Med. Imaging,” (International Society for Optics and Photonics, 2015), pp. 94133X.

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Figures (13)

Fig. 1
Fig. 1 (Best viewed in color) Epidermis and dermis are two of the major layers of the skin. In the figure, epidermis is bounded by the skin surface (green line) from above and dermo-epidermal junction (DEJ, red line) from below.
Fig. 2
Fig. 2 Upper row shows example of cases where skin layer segmentation is difficult. Such cases constitute the dataset SD2 (Section 4.1) (i) Upper arrow shows hair while lower arrow shows the dark region below hair. (ii)–(iv) also show similar cases of shadowing. (v)–(viii) show examples of B-scans from SD1 which do not suffer from shadowing (Section 4.1)
Fig. 3
Fig. 3 A flowchart of the proposed approach for skin surface segmentation and roughness estimation.
Fig. 4
Fig. 4 (Best viewed in color) The neighborhood used in forming the 3D graph is illustrated for the case when Δy = 2 and Δz = 1. The spheres represent the voxels of the graph. Neighbors for the voxel in green are shown in red. Note that in order to avoid clutter, the edges are not drawn.
Fig. 5
Fig. 5 The raw OCT volume was cropped out to remove regions with weak signal.
Fig. 6
Fig. 6 Preprocessing steps shown using a sample B-scan (Section 2.3). (i) Raw B-scan, (ii) The B-scan in (i) after conversion to 8-bit. (iii) Final output of preprocessing after contrast stretching of the 8-bit image. This output was used for cost computation.
Fig. 7
Fig. 7 (Best viewed in color) Effect of varying Δy. Green curve is the result of the proposed algorithm. Lower Δyy = 1) in (i) leads to a smoother curve but such curve does not fit well to the rugged surface shown. Instead Δy = 2 in (ii) fits better. Δz = 3 for both cases.
Fig. 8
Fig. 8 Process of furrow extraction. (i) Segmented skin surface, (ii) Depth map, and (iii) furrows extracted using Frangi filters (Section 3).
Fig. 9
Fig. 9 Computing furrow depth to account for tilted surfaces. The blue curve represents the tilted surface. μs is the reference surface level estimated by averaging the depths in the entire depth map while μs1 is the reference surface level along the tilted surface. For a furrow located at point P, the actual depth is NP instead of MP. Note that we have explained in 2D for ease of understanding however, the actual surface is in 3D.
Fig. 10
Fig. 10 The six volumes in SD3 as shown to the clinicians for ranking in order of furrow density and furrow depth (Section 4.1).
Fig. 11
Fig. 11 (Best viewed in color) Results for skin surface segmentation for the proposed approach as compared with related works. From left to right are ground truth and the results of A. Li et al. [21], K. Li et al. [12] and the proposed method, respectively. The unsigned mean vertical errors for each sample (from left to right respectively) are (i) 3.24, 3.46 and 2.38 pixels (ii) 11.45, 4.61 and 3.67 pixels.
Fig. 12
Fig. 12 A comparison of segmentation obtained using (i) 2D segmentation method of A. Li et al. [21] and (ii) the proposed method. The encircled regions show strands of hair incorrectly segmented by [21] as the skin surface. The proposed method, on the other hand, is more robust to the presence of hair.
Fig. 13
Fig. 13 Depth maps generated using the proposed approach (Top row) and the method in [22] (Bottom row) for the six volumes used for roughness estimation. The depth maps generated using the proposed method has fewer artifacts as compared to the depth maps produced by [22].

Tables (5)

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Table 1 Clinicians’ grading for furrow density. 1 denotes the surface with the least dense furrows while 3 is the surface with the densest furrows. Ci refers to the ith clinician while Vi refers to the ith volume.

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Table 2 Clinicians’ grading for furrow depth. 1 denotes the surface with the shallowest furrows while 3 is the surface with the deepest furrows.

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Table 3 Results of skin surface segmentation on SD1 and SD2. The proposed approach is compared with other related works. Best results are highlighted in bold. Δy = 2 and Δz = 1 for all the experiments. Errors are in pixels and 1 pixel ≃ 3 μm.

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Table 4 Results of roughness estimation on the six volumes (see the depth maps in Fig. 13) using the proposed method and a related work [22]. The values of the two parameters relating to the furrow density and furrow depth are reported.

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Table 5 Ranking of the volumes based on the roughness parameters computed using the proposed method and [22] (Refer to Table 4 for the values of the parameters). The ground truth (GT) is the mean ranking by the clinicians. Pearson’s correlation coefficients (ρ) show the degree of correlation between computed parameters and the ground truth (Section 4.3). For ground truth marking of furrow density, 1 is the least dense while 3 is the densest. For furrow depth, 1 is the shallowest while 3 is the deepest. Best results are highlighted in bold. NA: Not Applicable.

Equations (16)

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c Z ( x , y ) = ρ c Z edge ( x , y ) + ( 1 ρ ) c Z region ( x , y ) .
c Z edge ( x , y ) = p ( ϕ Z ( x , y ) ) .
p ( ϕ Z ( x , y ) ) = { 0 , if ϕ Z ( x , y ) = 270 ° 1 , if 0 ° ϕ Z ( x , y ) 180 ° < 1 , otherwise .
ϕ Z ( x , y ) = arctan ( G y G x ) .
c Z region ( x , y ) = b Z ( x , y ) + r Z ( x , y ) .
b Z ( x , y ) | x = x , y = y = y y t Z ( x , y ) .
t Z ( x , y ) ) = { 0 , if I ( x , y , z ) < θ 1 , otherwise .
r Z ( x , y ) | x = x , y = y = y y ( I ( x , y , Z ) a 1 2 ) + y > y ( I ( x , y , Z ) a 2 2 ) .
a ^ 1 ( x , y , Z ) = mean ( I ( x , y 1 , Z )
a ^ 2 ( x , y , Z ) = mean ( I ( x , y 2 , Z ) .
F density = N furrows N skin
F depth = f = 1 N furrows ( μ s D f ) N furrows
S 1 = W T ( S M )
W T = [ w 1 w 2 w 3 ]
F depth 1 = | f 1 = 1 N furrows 1 ( μ s 1 D f 1 ) | N furrows 1
E u = 1 N x ( x = 1 N x | y x pred y x G T | )

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