Abstract

Detecting skin lentigo in reflectance confocal microscopy images is an important and challenging problem. This imaging modality has not yet been widely investigated for this problem and there are a few automatic processing techniques. They are mostly based on machine learning approaches and rely on numerous classical image features that lead to high computational costs given the very large resolution of these images. This paper presents a detection method with very low computational complexity that is able to identify the skin depth at which the lentigo can be detected. The proposed method performs multiresolution decomposition of the image obtained at each skin depth. The distribution of image pixels at a given depth can be approximated accurately by a generalized Gaussian distribution whose parameters depend on the decomposition scale, resulting in a very-low-dimension parameter space. SVM classifiers are then investigated to classify the scale parameter of this distribution allowing real-time detection of lentigo. The method is applied to 45 healthy and lentigo patients from a clinical study, where sensitivity of 81.4% and specificity of 83.3% are achieved. Our results show that lentigo is identifiable at depths between 50μm and 60μm, corresponding to the average location of the the dermoepidermal junction. This result is in agreement with the clinical practices that characterize the lentigo by assessing the disorganization of the dermoepidermal junction.

© 2017 Optical Society of America

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References

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    [Crossref]
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    [Crossref]
  4. P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
    [Crossref] [PubMed]
  5. I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
    [Crossref]
  6. I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
    [Crossref]
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  8. J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
    [Crossref] [PubMed]
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  17. K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.
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    [Crossref]
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    [Crossref]
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  24. C. Q. Yuan, Z. Peng, and X. P. Yan, “Surface characterisation using wavelet theory and confocal laser scanning microscopy,” J. Tribol. 127, 394–404 (2005).
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  25. Y. Hu, A. Shen, T. Jiang, Y. Ai, and J. Hu, “Classification of normal and malignant human gastric mucosa tissue with confocal raman microspectroscopy and wavelet analysis,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 69(2), 378–382 (2008).
    [Crossref]
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2017 (2)

M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
[Crossref]

S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
[Crossref]

2016 (1)

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

2015 (2)

M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
[Crossref] [PubMed]

B. P. Hibler, M. Cordova, R. J. Wong, and A. M. Rossi, “Intraoperative real-time reflectance confocal microscopy for guiding surgical margins of lentigo maligna melanoma,” Dermatol. Surg. 41, 980–983 (2015).
[Crossref] [PubMed]

2014 (4)

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
[Crossref] [PubMed]

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

2013 (3)

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

R. Rakotomalala, “Comparaison de populations: Tests paramétriques,” Bartlett test,  727–29 (2013).

V. E. Johnson, “Revised standards for statistical evidence,” Proceedings of the National Academy of Sciences 110(48), 19313–19317 (2013).
[Crossref]

2012 (1)

S. Yu, A. Zhang, and H. Li, “A review of estimating the shape parameter of generalized Gaussian distribution,” J. Comput. Inf. Syst. 8(21), 9055–9064 (2012).

2011 (2)

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
[Crossref]

2009 (1)

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

2008 (4)

K. S. Nehal, D. Gareau, and M. Rajadhyaksha, “Skin imaging with reflectance confocal microscopy,” Seminars in Cutaneous Medecine and Surgery 27, 37–43 (2008).
[Crossref]

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
[Crossref] [PubMed]

Y. Hu, A. Shen, T. Jiang, Y. Ai, and J. Hu, “Classification of normal and malignant human gastric mucosa tissue with confocal raman microspectroscopy and wavelet analysis,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 69(2), 378–382 (2008).
[Crossref]

M. Wiltgen, A. Gerger, C. Wagner, and J. Smolle, “Automatic identification of diagnostic significant regions in confocal laser scanning microscopy of melanocytic skin tumors,” Methods Inf. Med. 47(1), 14–25 (2008).
[PubMed]

2005 (2)

C. Q. Yuan, Z. Peng, and X. P. Yan, “Surface characterisation using wavelet theory and confocal laser scanning microscopy,” J. Tribol. 127, 394–404 (2005).
[Crossref]

B. L. Luck, K. D. Carlson, A. C. Bovik, and R. R. Richards-Kortum, “An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue,” IEEE Trans. Image Processing 14(9), 1265–1276 (2005).
[Crossref]

2003 (1)

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Classification of melanoma using tree structured wavelet transforms,” Computer Methods and Programs in Biomedicine 72(3), 223–239 (2003).
[Crossref] [PubMed]

2002 (2)

M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance,” IEEE Trans. Image Processing 11(2), 146–158 (2002).
[Crossref]

A. Dima, M. Scholz, and K. Obermayer, “Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform,” IEEE Trans. Image Process 11, 790–801 (2002).
[Crossref]

1999 (2)

P. Moulin and J. Liu, “Analysis of multiresolution image denoising shemes using generalized Gaussian and complexity priors,” IEEE Trans. Inform. Theory. 45, 909–919 (1999).
[Crossref]

G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representation,” IEEE Trans. Image Proc. 8, 592–598 (1999).
[Crossref]

1995 (1)

K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Trans. Circ. Syst., Video Techn. 5, 52–56 (1995).
[Crossref]

1986 (1)

N. A. C. Cressie and H. J. Whitford, “How to use the two sample t-test,” Biometrical Journal 28(2), 131–148 (1986).
[Crossref]

Ahlgrimm-Siess, V.

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
[Crossref]

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

Ai, Y.

Y. Hu, A. Shen, T. Jiang, Y. Ai, and J. Hu, “Classification of normal and malignant human gastric mucosa tissue with confocal raman microspectroscopy and wavelet analysis,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 69(2), 378–382 (2008).
[Crossref]

Alarcon, I.

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

Alessi-Fox, C.

K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.

Alix, T.

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Alos, L.

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

Ardigo, M.

S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

Ardigò, M.

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

Batatia, H.

A. Halimi, H. Batatia, J L. Digabel, G. Josse, and J.-Y. Tourneret, “Technical report associated with the paper “Statistical modeling of reflectance confocal microscopy images and characterization of skin lentigo,” Tech. Rep., University of Toulouse, France, Feb2017.

Bovik, A. C.

B. L. Luck, K. D. Carlson, A. C. Bovik, and R. R. Richards-Kortum, “An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue,” IEEE Trans. Image Processing 14(9), 1265–1276 (2005).
[Crossref]

Bradley, A. P.

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

Brooks, D. H.

S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
[Crossref]

S. Kurugol, M. Rajadhyaksha, J. G. Dy, and D. H. Brooks, “Validation study of automated dermal/epidermal junction localization algorithm in reflectance confocal microscopy images of skin,” in SPIE BiOS. International Society for Optics and Photonics, 2012, 820702.

K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.

S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

Calzavara-Pinton, P.

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
[Crossref] [PubMed]

Cambazard, F.

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Carlson, K. D.

B. L. Luck, K. D. Carlson, A. C. Bovik, and R. R. Richards-Kortum, “An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue,” IEEE Trans. Image Processing 14(9), 1265–1276 (2005).
[Crossref]

Carrera, C.

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

Champin, J.

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Cinotti, E.

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Cordova, M.

B. P. Hibler, M. Cordova, R. J. Wong, and A. M. Rossi, “Intraoperative real-time reflectance confocal microscopy for guiding surgical margins of lentigo maligna melanoma,” Dermatol. Surg. 41, 980–983 (2015).
[Crossref] [PubMed]

Correa, C.

E. Somoza, G. O. Cula, C. Correa, and J. B. Hirsch, Automatic Localization of Skin Layers in Reflectance Confocal Microscopy, Springer International Publishing, Cham, 141–150 (2014).

Cressie, N. A. C.

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S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
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S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

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M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
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S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

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M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
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P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
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E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
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M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
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A. Halimi, H. Batatia, J L. Digabel, G. Josse, and J.-Y. Tourneret, “Technical report associated with the paper “Statistical modeling of reflectance confocal microscopy images and characterization of skin lentigo,” Tech. Rep., University of Toulouse, France, Feb2017.

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S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
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R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
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E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
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S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
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K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.

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S. Kurugol, M. Rajadhyaksha, J. G. Dy, and D. H. Brooks, “Validation study of automated dermal/epidermal junction localization algorithm in reflectance confocal microscopy images of skin,” in SPIE BiOS. International Society for Optics and Photonics, 2012, 820702.

S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

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M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
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M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
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I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
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I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
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M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
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P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
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M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
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K. S. Nehal, D. Gareau, and M. Rajadhyaksha, “Skin imaging with reflectance confocal microscopy,” Seminars in Cutaneous Medecine and Surgery 27, 37–43 (2008).
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Obermayer, K.

A. Dima, M. Scholz, and K. Obermayer, “Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform,” IEEE Trans. Image Process 11, 790–801 (2002).
[Crossref]

Palou, J.

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
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J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Patwardhan, S. V.

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Classification of melanoma using tree structured wavelet transforms,” Computer Methods and Programs in Biomedicine 72(3), 223–239 (2003).
[Crossref] [PubMed]

Pellacani, G.

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
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J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Prow, T. W.

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

Puig, S.

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
[Crossref]

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

Rajadhyaksha, M.

S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
[Crossref]

M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
[Crossref]

K. S. Nehal, D. Gareau, and M. Rajadhyaksha, “Skin imaging with reflectance confocal microscopy,” Seminars in Cutaneous Medecine and Surgery 27, 37–43 (2008).
[Crossref]

S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

S. Kurugol, M. Rajadhyaksha, J. G. Dy, and D. H. Brooks, “Validation study of automated dermal/epidermal junction localization algorithm in reflectance confocal microscopy images of skin,” in SPIE BiOS. International Society for Optics and Photonics, 2012, 820702.

K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.

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A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

Relue, P. A.

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Classification of melanoma using tree structured wavelet transforms,” Computer Methods and Programs in Biomedicine 72(3), 223–239 (2003).
[Crossref] [PubMed]

Richards-Kortum, R. R.

B. L. Luck, K. D. Carlson, A. C. Bovik, and R. R. Richards-Kortum, “An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue,” IEEE Trans. Image Processing 14(9), 1265–1276 (2005).
[Crossref]

Richtig, E.

E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
[Crossref]

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

Rossi, A.

M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
[Crossref]

Rossi, A. M.

B. P. Hibler, M. Cordova, R. J. Wong, and A. M. Rossi, “Intraoperative real-time reflectance confocal microscopy for guiding surgical margins of lentigo maligna melanoma,” Dermatol. Surg. 41, 980–983 (2015).
[Crossref] [PubMed]

Sala, R.

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
[Crossref] [PubMed]

Scheunders, P.

G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representation,” IEEE Trans. Image Proc. 8, 592–598 (1999).
[Crossref]

Scholz, M.

A. Dima, M. Scholz, and K. Obermayer, “Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform,” IEEE Trans. Image Process 11, 790–801 (2002).
[Crossref]

Scolyer, R. A.

P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
[Crossref] [PubMed]

Seguin, P.

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

Segura, S.

P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
[Crossref] [PubMed]

Sharifi, K.

K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Trans. Circ. Syst., Video Techn. 5, 52–56 (1995).
[Crossref]

Shen, A.

Y. Hu, A. Shen, T. Jiang, Y. Ai, and J. Hu, “Classification of normal and malignant human gastric mucosa tissue with confocal raman microspectroscopy and wavelet analysis,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 69(2), 378–382 (2008).
[Crossref]

Smolle, J.

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

M. Wiltgen, A. Gerger, C. Wagner, and J. Smolle, “Automatic identification of diagnostic significant regions in confocal laser scanning microscopy of melanocytic skin tumors,” Methods Inf. Med. 47(1), 14–25 (2008).
[PubMed]

Somoza, E.

E. Somoza, G. O. Cula, C. Correa, and J. B. Hirsch, Automatic Localization of Skin Layers in Reflectance Confocal Microscopy, Springer International Publishing, Cham, 141–150 (2014).

Soyer, H. P.

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

Tourneret, J.-Y.

A. Halimi, H. Batatia, J L. Digabel, G. Josse, and J.-Y. Tourneret, “Technical report associated with the paper “Statistical modeling of reflectance confocal microscopy images and characterization of skin lentigo,” Tech. Rep., University of Toulouse, France, Feb2017.

Van, A. N.

M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
[Crossref] [PubMed]

Venturini, M.

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
[Crossref] [PubMed]

Vetterli, M.

M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance,” IEEE Trans. Image Processing 11(2), 146–158 (2002).
[Crossref]

Wagner, C.

M. Wiltgen, A. Gerger, C. Wagner, and J. Smolle, “Automatic identification of diagnostic significant regions in confocal laser scanning microscopy of melanocytic skin tumors,” Methods Inf. Med. 47(1), 14–25 (2008).
[PubMed]

Weger, W.

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

Weissmann, J.

S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

Wendorf, C.

C. Wendorf, “Manuals for univariate and multivariate statistics,” University of WisconsinStevens Point, WI, 2004.

Whitford, H. J.

N. A. C. Cressie and H. J. Whitford, “How to use the two sample t-test,” Biometrical Journal 28(2), 131–148 (1986).
[Crossref]

Wiltgen, M.

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

M. Wiltgen, A. Gerger, C. Wagner, and J. Smolle, “Automatic identification of diagnostic significant regions in confocal laser scanning microscopy of melanocytic skin tumors,” Methods Inf. Med. 47(1), 14–25 (2008).
[PubMed]

Wong, R. J.

B. P. Hibler, M. Cordova, R. J. Wong, and A. M. Rossi, “Intraoperative real-time reflectance confocal microscopy for guiding surgical margins of lentigo maligna melanoma,” Dermatol. Surg. 41, 980–983 (2015).
[Crossref] [PubMed]

Wouwer, G. V.

G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representation,” IEEE Trans. Image Proc. 8, 592–598 (1999).
[Crossref]

Wurm, E.

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

Wurm, E. M. T.

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

Yan, X. P.

C. Q. Yuan, Z. Peng, and X. P. Yan, “Surface characterisation using wavelet theory and confocal laser scanning microscopy,” J. Tribol. 127, 394–404 (2005).
[Crossref]

Yu, S.

S. Yu, A. Zhang, and H. Li, “A review of estimating the shape parameter of generalized Gaussian distribution,” J. Comput. Inf. Syst. 8(21), 9055–9064 (2012).

Yuan, C. Q.

C. Q. Yuan, Z. Peng, and X. P. Yan, “Surface characterisation using wavelet theory and confocal laser scanning microscopy,” J. Tribol. 127, 394–404 (2005).
[Crossref]

Zhang, A.

S. Yu, A. Zhang, and H. Li, “A review of estimating the shape parameter of generalized Gaussian distribution,” J. Comput. Inf. Syst. 8(21), 9055–9064 (2012).

Zvyagin, A. V.

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

Acta Dermato Venereologica, (1)

E. Richtig, R. Hofmann-Wellenhof, D. Kopera, L. El-Shabrawi-Caelen, and V. Ahlgrimm-Siess, “In vivo analysis of solar lentigines by reflectance confocal microscopy before and after q-switched ruby laser treatment,” Acta Dermato Venereologica, 91(2), 164–168 (2011).
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R. Rakotomalala, “Comparaison de populations: Tests paramétriques,” Bartlett test,  727–29 (2013).

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N. A. C. Cressie and H. J. Whitford, “How to use the two sample t-test,” Biometrical Journal 28(2), 131–148 (1986).
[Crossref]

Br. J. Dermatol. (2)

P. Guitera, L. E. Haydu, S. W. Menzies, R. A. Scolyer, A. Hong, G. B. Fogarty, F. Gallardo, and S. Segura, “Surveillance for treatment failure of lentigo maligna with dermoscopy and in vivo confocal microscopy: new descriptors,” Br. J. Dermatol. 170(6), 1305–1312 (2014).
[Crossref] [PubMed]

I. Alarcon, C. Carrera, J. Palou, L. Alos, J. Malvehy, and S. Puig, “Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions,” Br. J. Dermatol. 170, 802–808 (2014).
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Computer Methods and Programs in Biomedicine (1)

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Classification of melanoma using tree structured wavelet transforms,” Computer Methods and Programs in Biomedicine 72(3), 223–239 (2003).
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Dermatol. Surg. (2)

J. Champin, J. L. Perrot, E. Cinotti, B. Labeille, C. Douchet, G. Parrau, F. Cambazard, P. Seguin, and T. Alix, “In vivo reflectance confocal microscopy to optimize the spaghetti technique for defining surgical margins of lentigo maligna,” Dermatol. Surg. 40(3), 247–256 (2014).
[Crossref] [PubMed]

B. P. Hibler, M. Cordova, R. J. Wong, and A. M. Rossi, “Intraoperative real-time reflectance confocal microscopy for guiding surgical margins of lentigo maligna melanoma,” Dermatol. Surg. 41, 980–983 (2015).
[Crossref] [PubMed]

Experimental Dermatology (1)

A. P. Raphael, T. A. Kelf, E. Wurm, A. V. Zvyagin, H. P. Soyer, and T. W. Prow, “Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment,” Experimental Dermatology 22(7), 458–463 (2013).
[Crossref] [PubMed]

IEEE Trans. Circ. Syst., Video Techn. (1)

K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Trans. Circ. Syst., Video Techn. 5, 52–56 (1995).
[Crossref]

IEEE Trans. Image Proc. (1)

G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representation,” IEEE Trans. Image Proc. 8, 592–598 (1999).
[Crossref]

IEEE Trans. Image Process (1)

A. Dima, M. Scholz, and K. Obermayer, “Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform,” IEEE Trans. Image Process 11, 790–801 (2002).
[Crossref]

IEEE Trans. Image Processing (3)

M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance,” IEEE Trans. Image Processing 11(2), 146–158 (2002).
[Crossref]

B. L. Luck, K. D. Carlson, A. C. Bovik, and R. R. Richards-Kortum, “An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue,” IEEE Trans. Image Processing 14(9), 1265–1276 (2005).
[Crossref]

S. Ghanta, M. I. Jordan, K. Kose, D. H. Brooks, M. Rajadhyaksha, and J. G. Dy, “A marked poisson process driven latent shape model for 3d segmentation of reflectance confocal microscopy image stacks of human skin,” IEEE Trans. Image Processing 26(1), 172–184 (2017).
[Crossref]

IEEE Trans. Inform. Theory. (1)

P. Moulin and J. Liu, “Analysis of multiresolution image denoising shemes using generalized Gaussian and complexity priors,” IEEE Trans. Inform. Theory. 45, 909–919 (1999).
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J. Am. Acd. Dermatol. (1)

I. Alarcon, C. Carrera, L. Alos, J. Palou, J. Malvehy, and S. Puig, “In vivo reflectance confocal microscopy to monitor the response of lentigo maligna to imiquimod,” J. Am. Acd. Dermatol. 71, 49–55 (2014).
[Crossref]

J. Comput. Inf. Syst. (1)

S. Yu, A. Zhang, and H. Li, “A review of estimating the shape parameter of generalized Gaussian distribution,” J. Comput. Inf. Syst. 8(21), 9055–9064 (2012).

J. Tribol. (1)

C. Q. Yuan, Z. Peng, and X. P. Yan, “Surface characterisation using wavelet theory and confocal laser scanning microscopy,” J. Tribol. 127, 394–404 (2005).
[Crossref]

Journal of the European Academy of Dermatology and Venereology (1)

S. Koller, M. Wiltgen, V. Ahlgrimm-Siess, W. Weger, R. Hofmann-Wellenhof, E. Richtig, J. Smolle, and A. Gerger, “In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours,” Journal of the European Academy of Dermatology and Venereology 25(5), 554–558 (2011).
[Crossref]

Lasers in Surg. Med. (1)

M. Rajadhyaksha, A. Marghoob, A. Rossi, A. C. Halpern, and K. S. Nehal, “Reflectance confocal microscopy of skin in vivo: From bench to bedside,” Lasers in Surg. Med. 49(1), 7–19 (2017).
[Crossref]

Methods Inf. Med. (1)

M. Wiltgen, A. Gerger, C. Wagner, and J. Smolle, “Automatic identification of diagnostic significant regions in confocal laser scanning microscopy of melanocytic skin tumors,” Methods Inf. Med. 47(1), 14–25 (2008).
[PubMed]

Photochem. Photobiol. (1)

P. Calzavara-Pinton, C. Longo, M. Venturini, R. Sala, and G. Pellacani, “Reflectance confocal microscopy for in vivo skin imaging,” Photochem. Photobiol. 84(6), 1421–1430 (2008).
[Crossref] [PubMed]

PloS one (1)

M. A. Harris, A. N. Van, B. H. Malik, J. M. Jabbour, and K. C. Maitland, “A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue,” PloS one 10(3), e0122368 (2015).
[Crossref] [PubMed]

PloS one, (1)

S. C. Hames, M. Ardigò, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Automated segmentation of skin strata in reflectance confocal microscopy depth stacks,” PloS one, 11(4), e0153208 (2016).
[Crossref]

Proceedings of the National Academy of Sciences (1)

V. E. Johnson, “Revised standards for statistical evidence,” Proceedings of the National Academy of Sciences 110(48), 19313–19317 (2013).
[Crossref]

Seminars in Cutaneous Medecine and Surgery (2)

K. S. Nehal, D. Gareau, and M. Rajadhyaksha, “Skin imaging with reflectance confocal microscopy,” Seminars in Cutaneous Medecine and Surgery 27, 37–43 (2008).
[Crossref]

R. Hofmann-Wellenhof, E. M. T. Wurm, V. Ahlgrimm-Siess, E. Richtig, S. Koller, J. Smolle, and A. Gerger, “Reflectance confocal microscopy state-of-art and research overview,” Seminars in Cutaneous Medecine and Surgery 28, 172–179 (2009).
[Crossref]

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (1)

Y. Hu, A. Shen, T. Jiang, Y. Ai, and J. Hu, “Classification of normal and malignant human gastric mucosa tissue with confocal raman microspectroscopy and wavelet analysis,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 69(2), 378–382 (2008).
[Crossref]

Other (8)

A. Halimi, H. Batatia, J L. Digabel, G. Josse, and J.-Y. Tourneret, “Technical report associated with the paper “Statistical modeling of reflectance confocal microscopy images and characterization of skin lentigo,” Tech. Rep., University of Toulouse, France, Feb2017.

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory ( Prentice-HallEnglewood Cliffs, NJ, 1993).

C. Wendorf, “Manuals for univariate and multivariate statistics,” University of WisconsinStevens Point, WI, 2004.

S. Kurugol, J. G Dy, M. Rajadhyaksha, K. W. Gossage, J. Weissmann, and D. H. Brooks, “Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin,” in SPIE BiOS. International Society for Optics and Photonics, 2011, 79041A.

S. Kurugol, M. Rajadhyaksha, J. G. Dy, and D. H. Brooks, “Validation study of automated dermal/epidermal junction localization algorithm in reflectance confocal microscopy images of skin,” in SPIE BiOS. International Society for Optics and Photonics, 2012, 820702.

K. Kose, C. Alessi-Fox, M. Gill, J. G. Dy, D. H. Brooks, and M. Rajadhyaksha, “A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo,” in SPIE BiOS. International Society for Optics and Photonics, 2016, 968908.

E. Somoza, G. O. Cula, C. Correa, and J. B. Hirsch, Automatic Localization of Skin Layers in Reflectance Confocal Microscopy, Springer International Publishing, Cham, 141–150 (2014).

S. C. Hames, M. Ardigo, H. P. Soyer, A. P. Bradley, and T. W. Prow, “Anatomical skin segmentation in reflectance confocal microscopy with weak labels,” in Digital image computing: techniques and applications (dICTA’2015), international conference on. IEEE, 2015, 1–8.

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Figures (11)

Fig. 1
Fig. 1

Block-diagram of the proposed method. It consists of 4 different stages. First, a wavelet decomposition is applied to the image. Second, GGD parameters (α, β) are estimated, along with the variance V and the entropy H. Third, statistical tests are implemented to identify characteristic depths. Finally, SVM classifiers based on the GGD parameters classify tissues as lentigo or healthy.

Fig. 2
Fig. 2

Typical images at the DEJ from six healthy (left to right and top to bottom #1, #5, #4, #3, #2, #6) and six lentigo (#33, #38, #40, #31, #44, #37) patients. One can observe coarse texture in the form of round shapes in the presence of lentigo. N.B. Values of the parameter β are explained in section 4.

Fig. 3
Fig. 3

Example of the first scale wavelet decomposition (right) of an RCM image (left). The decomposition has four bands: Approximation (A), Horizontal (H), Vertical (V), Diagonal (D). Applying the same scheme to the approximation gives the next decomposition scale. As explained in the text, our statistical method consists of estimating the GGD parameters (α, β) for bands (H, V, D) at each scale. These parameters are used for the characterization and classification of the underlying tissues.

Fig. 4
Fig. 4

Histograms of the scale and shape parameters, estimated from band H at depth 54μm, with their means and standard deviations for all healthy and lentigo patients. Similar histograms are obtained for other bands and scales.

Fig. 5
Fig. 5

Histograms of the wavelet coefficients from band H at the four scales and the corresponding estimated GGD distributions. The figure shows data from two arbitrary healthy and lentigo patients (#6 and #38 respectively) at three representative depths (one depth per column).

Fig. 6
Fig. 6

Assessment of the GGD fit to wavelet coefficients. Mean KS statistic for the whole population at some selected depths, shown by scale and band. Scores are very good for all configurations, although they increase with higher scales, due to sparser data.

Fig. 7
Fig. 7

Evolution of the average parameters α̂ and β̂ throughout the depth for the different bands at all scales. Values of α are too similar for healthy and lentigo patients and cannot be used for discrimination. The parameter β shows significant difference for depths between 31μm and 76μm, with maximal difference at around 50μm. Our conclusion is that this parameter β can discriminate healthy and lentigo skin tissues.

Fig. 8
Fig. 8

P-value (in − log scale) and Bayes factor of the T test for α. The weak scores show that α is clearly not a discriminant between healthy and lentigo images.

Fig. 9
Fig. 9

P-value (in −log scale) and Bayes factor (BF) of the T test for β. The third row contains zooms on the lower scores of the BF to clarify the significance threshold. Strong scores can be seen for depths between 31μm and 76μm. Highest scores are obtained with depths around 50μm. This confirms that β is a good discriminant function that can be used to separate healthy and lentigo images at these depths.

Fig. 10
Fig. 10

Characteristics depths (found to be between 48um and 63um according to the T-test) and DEJ depths associated with the 45 patients.

Fig. 11
Fig. 11

Examples of RCM images of lentigo and healthy patients classified by the SVM classifier.

Tables (3)

Tables Icon

Table 1 Depths where H . , . 0 ( β ) is rejected (t-score > T0.05 = 2.02); the corresponding p-value and Bayes factor (BF) are shown. The first row gives intervals of depths (min depth to max depth) where T-scores are significant. The second row shows depths giving highest T-scores (maximal T-score ∓ 10%). The third row shows the depths corresponding to the maximal T-score. P-values and Bayes factors corresponding to each depth are shown below.

Tables Icon

Table 2 Confusion matrices of SVM classifiers based on the vector of parameters (β49.5μm, β54μm, β58.5μm) from the three characteristic depths. Twenty different classifiers have been tested with the leave-one-out method for the H, V, D, and H, V, D bands at the four scales 1, 2, 3, 4 separately and combined (1, 2, 3, 4). Se and Sp stand for the sensitivity and specificity. One notices the good accuracy for all bands (there is no preferred direction, justifying the joint H, V, D band) especially with the first scale.

Tables Icon

Table 3 Confusion matrix of SVM classifiers based on Koller method using the same training and testing conditions as in Table.2. Features from images of the three characteristic depths have been concatenated in one feature vector. Slightly higher accuracy (77.7%) has been obtained when Koller’s method is applied only to depth 54μm.

Equations (5)

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

f ( x ; α , β ) = β 2 α Γ ( 1 / β ) exp ( | x α | β )
H b , s 0 : μ b , s ( S ) = μ b , s ( L ) ,
H b , s 1 : μ b , s ( S ) μ b , s ( L ) .
B F = ( ν + T ν + ( T ν γ * ) 2 ) ( n + m ) / 2
[ β H , 1 β H , 2 β H , 3 β H , 4 β V , 1 β V , 2 β V , 3 β V , 4 β D , 1 β D , 2 β D , 3 β D , 4 ]