Abstract

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

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

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2018 (3)

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2018,” CA: A Cancer J. for Clin. 18, 7–30 (2018).

M. Salmivuori, N. Neittaanmäki, I. Pölönen, L. Jeskanen, E. Snellman, and M. Grönroos, “Hyperspectral imaging system in the delineation of ill-defined basal cell carcinomas: a pilot study,” J. Eur. Acad. Dermatol. Venereol. 0, 0 (2018). Online Version of Record before inclusion in an issue.

Q. Wang, Q. Li, M. Zhou, L. Sun, S. Qiu, and Y. Wang, “Melanoma and melanocyte identification from hyperspectral pathology images using object-based multiscale analysis,” Appl. Spectrosc. 72, 1538–1547 (2018).
[PubMed]

2017 (7)

A. S. Farberg, R. R. Winkelmann, N. Tucker, R. White, and D. S. Rigel, “The impact of quantitative data provided by a multi-spectral digital skin lesion analysis device on dermatologists’ decisions to biopsy pigmented lesions,” J. Clin. Aesthetic Dermatol. 10, 24–26 (2017).

A. M. Witkowski, J. Ludzik, F. Arginelli, S. Bassoli, E. Benati, A. Casari, N. D. Carvalho, B. D. Pace, F. Farnetani, A. Losi, M. Manfredini, C. Reggiani, J. Malvehy, and G. Pellacani, “Improving diagnostic sensitivity of combined dermoscopy and reflectance confocal microscopy imaging through doble reader concordance evaluation in telemedicine settings: A retrospective study of 1000 equivocal cases,” PLoS One 12, 1–14 (2017).
[Crossref]

N. Neittaanmäki, M. Salmivuori, I. Pölönen, L. Jeskanen, A. Ranki, O. Saksela, e. Snellman, and M. Grönroos, “Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma,” Br. J. Dermatol. 177, 1742–1744 (2017).
[Crossref]

M. K. Tripp, M. Watson, S. J. Balk, S. M. Swetter, and J. E. Gershenwald, “State of the Science on Prevention and Screening to Reduce Melanoma Incidence and Mortality: The Time is Now,” CA Cancer J Clin. 66, 460–480 (2017).
[Crossref]

A. Pardo, E. Real, V. Krishnaswamy, J. M. L. Higuera, B. W. Pogue, and O. M. Conde, “Directional Kernel Density Estimation for Classification fo Breast Tissue Spectra,” IEEE Transactions on Med. Imaging 36, 64–73 (2017).
[Crossref]

T. Kim, M. A. Visbal-Onufrak, R. L. Konger, and Y. L. Kim, “Data-driven imaging of tissue inflammation using rgb-based hyperspectral reconstruction toward personal monitoring of dermatologic health,” Biomed. Opt. Express 8, 5282–5296 (2017).
[Crossref] [PubMed]

F. Vasefi, N. MacKinnon, N. Booth, A. J. Durkin, and D. Farkas, “Melanoma surveillance by multimode, hyperspectral dermoscopy and self-imaging using smartphone in high-risk patients,” J. Am. Acad. Dermatol. 76, AB168 (2017).
[Crossref]

2016 (1)

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
[Crossref] [PubMed]

2015 (1)

N. Neittaanmäki-Perttu, M. Grönroos, L. Jeskanen, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Delineating margins of lentigo maligna using a hyperspectral imaging system,” Acta Derm Venereol 95, 549–552 (2015).
[Crossref]

2014 (1)

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19, 010901 (2014).
[Crossref]

2013 (5)

A. Eguizábal, A. M. Laughney, P. B. García-Allende, V. Krishnaswamy, W. A. Wells, K. D. Paulsen, B. W. Pogue, J. M. López-Higuera, and O. M. Conde, “Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements,” Biomed. Opt. Express 4, 1104–1118 (2013).
[Crossref] [PubMed]

T. Nagaoka, A. Nakamura, H. Okutani, Y. Kiyohara, H. Koga, T. Saida, and T. Sota, “Hyperspectroscopic screening of melanoma on acral volar skin,” Ski. Res. Technol. 19, e290–e296 (2013).
[Crossref]

N. Neittaanmäki-Perttu, M. Grönroos, T. Tani, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Detecting field cancerization using a hyperspectral imaging system,” Lasers Surg. Medicine 45, 410–417 (2013).
[Crossref]

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Medicine Biol. 58, R37–R61 (2013).
[Crossref]

Y. Cui, J. Yang, Y. Yamaguchi, G. Singh, S.-E. Park, and H. Kobayashi, “On Semiparametric Clutter Estimation for Ship Detection in Synthetic Aperture Radar Images,” IEEE Transactions on Geoscience and Remote Sensing 51, 3170–3180 (2013).
[Crossref]

2012 (5)

C. Curiel-Lewandrowski, S. C. Chen, and S. M. Swetter, “Screening and Prevention Measures for Melanoma: Is There a Survival Advantage?” Curr Oncol Rep 14, 458–467 (2012).
[Crossref] [PubMed]

G. P. Ga, D. U. Ekwueme, F. K. Tankga, and L. C. Richardson, “Melanoma Treatment Costs: A Systematic Review of the Literature, 1990–2011,” Am J Prev Med. 43, 537–545 (2012).
[Crossref]

L. K. Ferris and R. J. Harris, “New diagnostic aides for melanoma,” Dermatol. Clin. 30, 535–545 (2012).
[Crossref] [PubMed]

I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3, 467–472 (2012).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res. 18, 6315–6325 (2012).
[Crossref] [PubMed]

2011 (3)

I. Kuzmina, I. Diebele, J. Jakovels, J. Spigulis, L. Valeine, J. Kapostinsh, and A. Berzina, “Towards noncontact skin melanoma selection by multispectral imaging analysis,” J. Biomed. Opt. 16, 060502 (2011).
[Crossref]

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci 102, 852–857 (2011).
[Crossref] [PubMed]

A. Villa, J. Benediktsson, J. Chanussot, and C. Jutten, “Hyperspectral Image Classification with Independent Component Discriminant Analysis,” IEEE Transactions on Geoscience and Remote Sensing 49, 4865–4876 (2011).
[Crossref]

2010 (2)

J. D. Emery, J. Hunter, P. N. Hall, A. J. Watson, M. Moncrieff, and F. M. Walter, “Accuracy of siascopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm,” BMC Dermatol. 10, 9 (2010).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. García-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt. 15066019 (2010).
[Crossref]

2009 (4)

P. B. García-Allende, V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, O. M. Conde, and B. W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009).
[Crossref] [PubMed]

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis using microsampling reflectance spectroscopy,” J. Biomed. Opt. 14, 014004 (2009).
[Crossref]

J. McMudry, G. Jay, S. Suner, and G. Crawford, “Photonics-based in vivo total hemoglobin monitoring and clinical relevance,” J. Biophotonics 2, 277–287 (2009).
[Crossref]

R. Marchesini, A. Bono, and M. Carrara, “In vivo characterization of melanin in melanocytic lesions: spectroscopic study on 1671 pigmented skin lesions,” J. Biomed. Opt. 14, 014027 (2009).
[Crossref] [PubMed]

2005 (1)

S. Tomatis, M. Carrara, A. Bono, C. Bartoli, M. Lualdi, G. Tragni, A. Colombo, and R. Marchesini, “Automated melanoma detection with a novel multispectral imaging system: results of a prospective study,” Phys. Medicine Biol. 50, 1675–1687 (2005).
[Crossref]

2002 (1)

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proc. IEEE 90, 1151–1163 (2002).
[Crossref]

2000 (1)

A. Levy and M. Lindenbaum, “Sequential Karhunen-Loeve basis extraction and its application to images,” IEEE Transactions on Image Process. 9, 1371–1374 (2000).
[Crossref]

1999 (1)

C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen, “On clustering fmri time series,” NeuroImage 9, 298–310 (1999).
[Crossref] [PubMed]

1996 (1)

E. Seaman and R. A. Powell, “An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis,” Ecology 77, 2075–2085 (1996).
[Crossref]

1989 (1)

1985 (1)

J. S. Vitter, “Random sampling with a reservoir,” ACM Transactions on Math. Softw. (TOMS) 11, 37–57 (1985).
[Crossref]

Akbari, H.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci 102, 852–857 (2011).
[Crossref] [PubMed]

Aplin, A. E.

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P. B. García-Allende, V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, O. M. Conde, and B. W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009).
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S. A. Leachman, P. B. Cassidy, S. C. Chen, C. Curiel, A. Geller, D. Gareau, G. Pellacani, J. M. Grichnik, J. Malvehy, J. North, S. L. Jacques, T. Petrie, S. Puig, S. M. Swetter, S. Tofte, and M. A. Weinstock, “Methods of melanoma detection,” in Melanoma (Cancer Treatment and Research, volume 167), H. L. Kaufman and J. M. Menhert, eds. (Springer, 2015), chap. 3, pp. 51–105.

Geller, A.

S. A. Leachman, P. B. Cassidy, S. C. Chen, C. Curiel, A. Geller, D. Gareau, G. Pellacani, J. M. Grichnik, J. Malvehy, J. North, S. L. Jacques, T. Petrie, S. Puig, S. M. Swetter, S. Tofte, and M. A. Weinstock, “Methods of melanoma detection,” in Melanoma (Cancer Treatment and Research, volume 167), H. L. Kaufman and J. M. Menhert, eds. (Springer, 2015), chap. 3, pp. 51–105.

Gershenwald, J.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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S. A. Leachman, P. B. Cassidy, S. C. Chen, C. Curiel, A. Geller, D. Gareau, G. Pellacani, J. M. Grichnik, J. Malvehy, J. North, S. L. Jacques, T. Petrie, S. Puig, S. M. Swetter, S. Tofte, and M. A. Weinstock, “Methods of melanoma detection,” in Melanoma (Cancer Treatment and Research, volume 167), H. L. Kaufman and J. M. Menhert, eds. (Springer, 2015), chap. 3, pp. 51–105.

Grönroos, M.

M. Salmivuori, N. Neittaanmäki, I. Pölönen, L. Jeskanen, E. Snellman, and M. Grönroos, “Hyperspectral imaging system in the delineation of ill-defined basal cell carcinomas: a pilot study,” J. Eur. Acad. Dermatol. Venereol. 0, 0 (2018). Online Version of Record before inclusion in an issue.

N. Neittaanmäki, M. Salmivuori, I. Pölönen, L. Jeskanen, A. Ranki, O. Saksela, e. Snellman, and M. Grönroos, “Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma,” Br. J. Dermatol. 177, 1742–1744 (2017).
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N. Neittaanmäki-Perttu, M. Grönroos, L. Jeskanen, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Delineating margins of lentigo maligna using a hyperspectral imaging system,” Acta Derm Venereol 95, 549–552 (2015).
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N. Neittaanmäki-Perttu, M. Grönroos, T. Tani, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Detecting field cancerization using a hyperspectral imaging system,” Lasers Surg. Medicine 45, 410–417 (2013).
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J. D. Emery, J. Hunter, P. N. Hall, A. J. Watson, M. Moncrieff, and F. M. Walter, “Accuracy of siascopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm,” BMC Dermatol. 10, 9 (2010).
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G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen, “On clustering fmri time series,” NeuroImage 9, 298–310 (1999).
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Herlyn, D.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Herlyn, M.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Higuera, J. M. L.

A. Pardo, E. Real, V. Krishnaswamy, J. M. L. Higuera, B. W. Pogue, and O. M. Conde, “Directional Kernel Density Estimation for Classification fo Breast Tissue Spectra,” IEEE Transactions on Med. Imaging 36, 64–73 (2017).
[Crossref]

Hoopes, P. J.

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis using microsampling reflectance spectroscopy,” J. Biomed. Opt. 14, 014004 (2009).
[Crossref]

P. B. García-Allende, V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, O. M. Conde, and B. W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009).
[Crossref] [PubMed]

Hunter, J.

J. D. Emery, J. Hunter, P. N. Hall, A. J. Watson, M. Moncrieff, and F. M. Walter, “Accuracy of siascopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm,” BMC Dermatol. 10, 9 (2010).
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Jakovels, D.

I. Kuzmina, I. Diebele, L. Valeine, D. Jakovels, A. Kempele, J. Kapostinsh, and J. Spigulis, “Multi-spectral imaging analysis of pigmented and vascular skin lesions: results of a clinical trial,” in Photonic Therapeutics and Diagnostics VII, vol. 7883 (2011).
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I. Kuzmina, I. Diebele, J. Jakovels, J. Spigulis, L. Valeine, J. Kapostinsh, and A. Berzina, “Towards noncontact skin melanoma selection by multispectral imaging analysis,” J. Biomed. Opt. 16, 060502 (2011).
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M. Salmivuori, N. Neittaanmäki, I. Pölönen, L. Jeskanen, E. Snellman, and M. Grönroos, “Hyperspectral imaging system in the delineation of ill-defined basal cell carcinomas: a pilot study,” J. Eur. Acad. Dermatol. Venereol. 0, 0 (2018). Online Version of Record before inclusion in an issue.

N. Neittaanmäki, M. Salmivuori, I. Pölönen, L. Jeskanen, A. Ranki, O. Saksela, e. Snellman, and M. Grönroos, “Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma,” Br. J. Dermatol. 177, 1742–1744 (2017).
[Crossref]

N. Neittaanmäki-Perttu, M. Grönroos, L. Jeskanen, I. Pölönen, A. Ranki, O. Saksela, and E. Snellman, “Delineating margins of lentigo maligna using a hyperspectral imaging system,” Acta Derm Venereol 95, 549–552 (2015).
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A. Villa, J. Benediktsson, J. Chanussot, and C. Jutten, “Hyperspectral Image Classification with Independent Component Discriminant Analysis,” IEEE Transactions on Geoscience and Remote Sensing 49, 4865–4876 (2011).
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Kapostinsh, J.

I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3, 467–472 (2012).
[Crossref] [PubMed]

I. Kuzmina, I. Diebele, J. Jakovels, J. Spigulis, L. Valeine, J. Kapostinsh, and A. Berzina, “Towards noncontact skin melanoma selection by multispectral imaging analysis,” J. Biomed. Opt. 16, 060502 (2011).
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I. Diebele, I. Kuzmina, J. Kapostinsh, A. Derjabo, and J. Spigulis, “Melanoma-nevus differentiation by multispectral imaging,” in Clinical and Biomedical Spectroscopy and Imaging II, vol. 8087 (SPIE-OSA, 2011).

I. Kuzmina, I. Diebele, L. Valeine, D. Jakovels, A. Kempele, J. Kapostinsh, and J. Spigulis, “Multi-spectral imaging analysis of pigmented and vascular skin lesions: results of a clinical trial,” in Photonic Therapeutics and Diagnostics VII, vol. 7883 (2011).
[Crossref]

I. Diebele, A. Bekina, A. Derjabo, J. Kapostinsh, I. Kuzmina, and J. Spigulis, “Analysis of skin basalioma and melanoma by multispectral imaging,” in Proceedings of SPIE, vol. 8427 (2012).
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Karakousis, G. C.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Kempele, A.

I. Kuzmina, I. Diebele, L. Valeine, D. Jakovels, A. Kempele, J. Kapostinsh, and J. Spigulis, “Multi-spectral imaging analysis of pigmented and vascular skin lesions: results of a clinical trial,” in Photonic Therapeutics and Diagnostics VII, vol. 7883 (2011).
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Kim, T.

Kim, Y. L.

Kirkwood, J. M.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Koga, H.

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Krauthammer, M.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
[Crossref] [PubMed]

Krishnaswamy, V.

A. Pardo, E. Real, V. Krishnaswamy, J. M. L. Higuera, B. W. Pogue, and O. M. Conde, “Directional Kernel Density Estimation for Classification fo Breast Tissue Spectra,” IEEE Transactions on Med. Imaging 36, 64–73 (2017).
[Crossref]

A. Eguizábal, A. M. Laughney, P. B. García-Allende, V. Krishnaswamy, W. A. Wells, K. D. Paulsen, B. W. Pogue, J. M. López-Higuera, and O. M. Conde, “Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements,” Biomed. Opt. Express 4, 1104–1118 (2013).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res. 18, 6315–6325 (2012).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. García-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt. 15066019 (2010).
[Crossref]

P. B. García-Allende, V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, O. M. Conde, and B. W. Pogue, “Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures,” J. Biomed. Opt. 14, 034034 (2009).
[Crossref] [PubMed]

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis using microsampling reflectance spectroscopy,” J. Biomed. Opt. 14, 014004 (2009).
[Crossref]

Kuzmina, I.

I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3, 467–472 (2012).
[Crossref] [PubMed]

I. Kuzmina, I. Diebele, J. Jakovels, J. Spigulis, L. Valeine, J. Kapostinsh, and A. Berzina, “Towards noncontact skin melanoma selection by multispectral imaging analysis,” J. Biomed. Opt. 16, 060502 (2011).
[Crossref]

I. Kuzmina, I. Diebele, L. Valeine, D. Jakovels, A. Kempele, J. Kapostinsh, and J. Spigulis, “Multi-spectral imaging analysis of pigmented and vascular skin lesions: results of a clinical trial,” in Photonic Therapeutics and Diagnostics VII, vol. 7883 (2011).
[Crossref]

I. Diebele, A. Bekina, A. Derjabo, J. Kapostinsh, I. Kuzmina, and J. Spigulis, “Analysis of skin basalioma and melanoma by multispectral imaging,” in Proceedings of SPIE, vol. 8427 (2012).
[Crossref]

I. Diebele, I. Kuzmina, J. Kapostinsh, A. Derjabo, and J. Spigulis, “Melanoma-nevus differentiation by multispectral imaging,” in Clinical and Biomedical Spectroscopy and Imaging II, vol. 8087 (SPIE-OSA, 2011).

Laughney, A. M.

A. Eguizábal, A. M. Laughney, P. B. García-Allende, V. Krishnaswamy, W. A. Wells, K. D. Paulsen, B. W. Pogue, J. M. López-Higuera, and O. M. Conde, “Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements,” Biomed. Opt. Express 4, 1104–1118 (2013).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res. 18, 6315–6325 (2012).
[Crossref] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. García-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt. 15066019 (2010).
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G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Valeine, L.

I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3, 467–472 (2012).
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I. Kuzmina, I. Diebele, L. Valeine, D. Jakovels, A. Kempele, J. Kapostinsh, and J. Spigulis, “Multi-spectral imaging analysis of pigmented and vascular skin lesions: results of a clinical trial,” in Photonic Therapeutics and Diagnostics VII, vol. 7883 (2011).
[Crossref]

Vasefi, F.

F. Vasefi, N. MacKinnon, N. Booth, A. J. Durkin, and D. Farkas, “Melanoma surveillance by multimode, hyperspectral dermoscopy and self-imaging using smartphone in high-risk patients,” J. Am. Acad. Dermatol. 76, AB168 (2017).
[Crossref]

Villa, A.

A. Villa, J. Benediktsson, J. Chanussot, and C. Jutten, “Hyperspectral Image Classification with Independent Component Discriminant Analysis,” IEEE Transactions on Geoscience and Remote Sensing 49, 4865–4876 (2011).
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J. D. Emery, J. Hunter, P. N. Hall, A. J. Watson, M. Moncrieff, and F. M. Walter, “Accuracy of siascopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm,” BMC Dermatol. 10, 9 (2010).
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Wang, Q.

Wang, Y.

Wargo, J. A.

G. Merlino, M. Herlyn, D. E. Fisher, B. C. Bastian, K. T. Flaherty, M. A. Davies, J. A. Wargo, C. Curiel-Lewandrowski, M. J. Weber, S. A. Leachman, M. S. Soengas, M. McMahon, J. W. Harbour, S. M. Swetter, A. E. Aplin, M. B. Atkins, M. W. Bosenberg, R. Dummer, J. Gershenwald, A. C. Halpern, D. Herlyn, G. C. Karakousis, J. M. Kirkwood, M. Krauthammer, roger S. Lo, G. V. Long, G. McArthur, A. Ribas, L. Schuchter, J. A. Sosman, K. S. Smalley, P. Steeg, N. E. Thomas, H. Tsao, T. Tueting, A. Weeraratna, G. Xu, R. Lomax, A. Martin, S. Silverstein, T. Turnham, and Z. A. Ronai, “The State of Melanoma: Challenges and Opportunities,” Pigment. Cell Melanoma Res. 29, 404–416 (2016).
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Figures (10)

Fig. 1
Fig. 1 General schematic description of the workflow and modes of operation implemented for the reservoir-based kernel density classifier. During learning (in blue) spectra-label pairs (a′t, l′t) are used to update a feature space vector basis B = (u1, . . ., uL) via the SKL algorithm and Equation (6), sorted by label and introduced into random reservoirs, where they have a chance of staying as reference spectra {rk,i}. During evaluation (in magenta), a new incoming spectrum at is projected onto feature space, where it is compared with reference spectra. For that to be possible, reference spectra must also be projected onto B. The likelihood of at belonging to each tissue type is evaluated through KDE, and a final diagnosis l̂t is calculated.

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Fig. 2
Fig. 2 First five left singular vectors, namely u1, . . ., u5, of the HSI melanoma feature space (left) and cumulative sum of explained variance (square of singular values).

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Fig. 3
Fig. 3 Data clouds of the dataset in the basis defined by u1, u2 and u3 (4000-point reservoir per category). The scatter plots are 2D and they display (a) the first two dimensions in feature space, (b) the second and third dimensions in feature space, and (c) the third and first dimensions in feature space, as given by their coordinates (α1, α2, α3). The data lay in an L-dimensional sphere due to the Standard Normal Variate.

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Fig. 4
Fig. 4 Average values (solid line) and standard deviation (shaded regions) of spectra within reservoirs of size 4000 for each tissue class considered: nevi (left), melanoma (center) and healthy skin (right).

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Fig. 5
Fig. 5 Elbow method segmentation example for Patient 2, Sample 2. On the left plot, values of ρ, (in blue), its derivative (in green and black) and the elbow point chosen by the criterion in Equation (15). The center and right subplots show the segmentation given by the elbow method and standard maximum likelihood classification (i.e. γ0 = γ1 = γ2), respectively.

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Fig. 6
Fig. 6 Top row. CIE 1931 RGB reconstruction of Sample 24, a nevus (left), as well as the in-lesion and out-of-lesion ROIs (center); these ROIs aren’t used in the classification result (shown as a semitransparent overlay on the right subplot). Bottom row. False-color likelihood maps for the three hypotheses (from left to right, H0 : Nevus, H1 : Melanoma, and H2 : Healthy Skin); note that each figure is complemented with a color bar with a different scale, otherwise the likelihood map for melanoma would be too dark to see. Since the likelihood of being melanoma is lower than the likelihood of being nevi in each pixel, they are classified as non-malignant.

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Fig. 7
Fig. 7 Top row. CIE 1931 RGB reconstruction of Sample 67, a melanoma (left), its in-lesion and out-of-lesion ROIs (center) and its final classification/segmentation output as a semitransparent overlay (right). Bottom row. False-color likelihood maps for the three hypotheses (from left to right, H0 : Nevus, H1 : Melanoma, and H2 : Healthy Skin) for this sample, with their accompanying color bars. The likelihood of pixels in the lesion for the nevi and skin hypotheses is zero, and therefore most pixels within the lesion are classified as more likely to be melanoma, while fringe regions in between skin and lesion are considered just pigmented, due to their likelihood values in feature space.

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Fig. 8
Fig. 8 System Receiver Operating Characteristic (ROC, left) and total accuracy as a function of γ1 (right), for different diagnostic ratios in leave-one-out cross-validation, namely 0.01, 0.05, 0.1, and 0.2.

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Fig. 9
Fig. 9 Boxplot with the difference between the average f1 and f0 values inside each lesion, f1 − f0, organized by pathology class. The difference between average class likelihoods per sample is smaller in nevi, due to the fact that their likelihood for being melanoma f(x|H1) is lower. This variation in likelihood functions is the key to the diagnostic capability of our procedure.

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Fig. 10
Fig. 10 Consistency in PDF estimates with random reservoirs. Average variance of pixel PDF values among machines of identical reservoir size (left subplot); per-sample variance in diagnostic ratio among machines of identical reservoir size for malignant (center) and benign lesions (right subplot). Probability density function and diagnosis variability are generally negligible, but they are stabilized once reservoir size is in the order of 2000–3000 reference samples, meaning that these sizes provide sufficient statistically and clinically significant information.

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Tables (3)

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Table 1 Number of patients per pathology and total number of samples per category in the dataset.

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Table 2 Overall system performance for different cross-validation (CV) settings (leave-one-out, 10-fold and 3-fold) given a diagnostic ratio of p = 0.05 and maximizing for accuracy. For 3-fold and 10-fold CV, half the standard deviation of each parameter is also indicated.

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Table 3 Overall system performance for different cross-validation (CV) settings (leave-one-out, 10-fold and 3-fold) given a diagnostic ratio of p = 0.05, and minimizing false negative rates (FNR). For 3-fold and 10-fold CV, half the standard deviation of each parameter is also indicated.

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Equations (16)

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OD = − log 10 ( I ( λ ) I 0 ( λ ) ) = − log 10 ( R ( λ ) ) ,
x ′ = x − μ σ .
A = U Σ V T
A = E 1 + E 2 + ⋯ + E r = ∑ k = 1 r u k σ k v k T ,
‖ A − A ˜ L ‖ F ≤ σ L + 1
a t ≈ a ˜ L , t = ∑ i = i L u i α t , i = ∑ i = 1 L u i 〈 u i , a t 〉 ,
σ contrib ( k ) = ∑ i = 1 k σ i 2 − ∑ i = 1 k − 1 σ i 2 ∑ i = 1 k − 1 σ i 2
f ˜ ( x | H k ) = 1 n ∑ i = 1 n K ( x − X k , i ) ,
K ( x ) = ( 2 π ) − d / 2 | H | − 1 / 2 e − 1 2 x T H − 1 x ,
h i j = { σ i ( 4 d + 2 ) 1 d + 4 n − 1 d + 4 , i = j , 0 , i ≠ j ,
f ( x t | H k ) P ( H k ) > H k f ( x t | H i ) P ( H i ) , ∀ i ≠ k .
f ( x t | H k ) > H k f ( x t | H i ) , ∀ i ≠ k ,
b ^ t = arg   max i { f ( x t | H i ) } ,
b ^ t = arg   max i { f ( x t | H i ) γ i } .
ρ ( γ 2 ) = N les ( γ 2 ) N total ∈ [ 0 , 1 ] ,
γ 2 , opt = { γ 2 : ρ ″ ( γ 2 ) ρ max ″ ≤ 0.01 } ,

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