Abstract

Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method.

© 2017 Optical Society of America

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2015 (2)

S. E. Godoy, D. A. Ramirez, S. A. Myers, G. von Winckel, S. S. Krishna, R. S. Padilla, P. Sen, and S. Krishna, “Dynamic Infrared Imaging for Skin Cancer Screening,” Infrared Phys. Techn. 70, 147–152 (2015).
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[Crossref] [PubMed]

2014 (4)

A. Saez, C. Serrano, and B. Acha, “Model-based classification methods of global patterns in dermoscopic images,” IEEE Trans. Med. Imaging 33, 1137–1147 (2014).
[Crossref] [PubMed]

G. Pellacani, B. D. Pace, C. Reggiani, A. M. Cesinaro, G. Argenziano, I. Zalaudek, H. P. Soyer, and C. Longo, “Distinct melanoma types based on reflectance confocal microscopy,” Experimental Dermatology 23, 414–418 (2014).
[Crossref] [PubMed]

L. Themstrup, C. Banzhaf, M. Mogensen, and G. Jemec, “Optical coherence tomography imaging of non-melanoma skin cancer undergoing photodynamic therapy reveals subclinical residual lesions,” Photodiagnosis Photodyn. Ther. 11, 7–12 (2014).
[Crossref]

J. E. Mayer, S. M. Swetter, T. Fu, and A. C. Geller, “Screening, early detection, education, and trends for melanoma: Current status (2007–2013) and future directions: Part II. Screening, education, and future directions,” Journal of the American Academy of Dermatology 71, 611 (2014).
[Crossref]

2013 (3)

C. Herman, “The role of dynamic infrared imaging in melanoma diagnosis,” Expert Rev. Dermatol. 8, 177–184 (2013).
[Crossref] [PubMed]

I. Quinzan, J. M. Sotoca, P. Latorre-Carmona, F. Pla, P. Garcia-Sevilla, and E. Boldo, “Band selection in spectral imaging for non-invasive melanoma diagnosis,” Biomed. Opt. Express 4, 514–519 (2013).
[Crossref] [PubMed]

A. R. Cukras, “On the comparison of diagnosis and management of melanoma between dermatologists and melafind,” JAMA Dermatology 149, 622–623 (2013).
[Crossref] [PubMed]

2012 (7)

B. B. Lahiri, S. Bagavathiappan, T. Jayakuman, and J. Phillip, “Medical applications of infrared thermography: A review,” Infrared Phys. Techn. 55, 221–235 (2012).
[Crossref]

R. Wells, D. Gutkowicz-Krusin, E. Veledar, A. Toledano, and S. C. Chen, “Comparison of diagnostic and management sensitivity to melanoma between dermatologists and melafind: A pilot study,”, Arch. Dermatol. 148, 1083–1084 (2012).
[Crossref] [PubMed]

R. Kleinerman, T. B. Whang, R. L. Bard, and E. S. Marmur, “Ultrasound in dermatology: Principles and applications,” J. American Academy of Dermatology 67, 478–487 (2012).
[Crossref]

R. E. Hunger, R. D. Torre, A. Serov, and T. Hunziker, “Assessment of melanocytic skin lesions with a high-definition laser doppler imaging system,” Skin Res. Technol. 18, 207–211 (2012).
[Crossref]

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

G. Zerovnik, A. Trkov, and I. A. Kodeli, “Correlated random sampling for multivariate normal and log-normal distributions,” Nucl. Instrum. Methods Phys. Res. A 690, 75–78 (2012).
[Crossref]

D. S. Rigel, M. Roy, J. Yoo, C. J. Cockerell, J. K. Robinson, and R. White, “Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma,” Arch. Dermatol. 148, 541–543 (2012).
[Crossref] [PubMed]

2011 (3)

M. P. Cetingul and C. Herman, “Quantification of the thermal signature of a melanoma lesion,” International Journal of Thermal Sciences 50, 421–431 (2011).
[Crossref]

M. P. Cetingul and C. Herman, “The Assessment of Melanoma Risk Using the Dynamic Infrared Imaging Technique,” Journal of Thermal Science and Engineering Applications 3, 031006 (2011).
[Crossref]

Z. Hamdoon, W. Jerjes, T. Upile, and C. Hopper, “Optical coherence tomography-guided photodynamic therapy for skin cancer: Case study,” Photodiagnosis Photodyn. Ther. 8, 49–52 (2011).
[Crossref] [PubMed]

2010 (2)

M. P. Cetingul and C. Herman, “A heat transfer model of skin tissue for the detection of lesions: sensitivity analysis,” Phys. Med. Biol. 55, 5933–5951 (2010).
[Crossref] [PubMed]

H. W. Rogers, M. A. Weinstock, A. R. Harris, M. R. Hinckley, S. R. Feldman, A. B. Fleischer, and B. M. Coldiron, “Incidence estimate of nonmelanoma skin cancer in the United States, 2006,” Arch. Dermatol. 146, 283–287 (2010).
[Crossref] [PubMed]

2009 (1)

N. Chumchob and K. Chen, “A Robust Affine Image Registration Method,” Int. J. Numer. Anal. Model. 6, 311–334 (2009).

2008 (3)

S. E. Godoy, J. E. Pezoa, and S. N. Torres, “Noise-cancellation-based nonuniformity correction algorithm for infrared focal-plane arrays,” Appl. Opt. 47, 5394–5399 (2008).
[Crossref] [PubMed]

M. Yang, “Normal Log-normal Mixture: Leptokurtosis, Skewness and Applications,” Appl. Econ. Lett. 15, 737–742 (2008).
[Crossref]

A. Dancey, B. Mahon, and S. Rayatt, “A review of diagnostic imaging in melanoma,” J. Plast. Reconstr. Aesthet. Surg. 61, 1275–1283 (2008).
[Crossref] [PubMed]

2007 (1)

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

2006 (1)

N. Nishida, H. Yano, T. Nishida, T. Kamura, and M. Kojiro, “Angiogenesis in Cancer,” Vascular Health and Risk Management 2, 213–219 (2006).
[Crossref]

2005 (1)

H. G. Welch, S. Woloshin, and L. M. Schwartz, “Skin biopsy rates and incidence of melanoma: population based ecological study,” BMJ 331, 481 (2005).
[Crossref] [PubMed]

2004 (1)

N. R. Abbasi, H. M. Shaw, D. S. Rigel, R. J. Friedman, W. McCarthy, I. Osman, A. W. Kopf, and D. Polsky, “Early diagnosis of cutaneous melanoma: Revisiting the ABCDE criteria,” JAMA 292, 2771–2776 (2004).
[Crossref] [PubMed]

2001 (1)

M. A. Fauci, R. Breiter, W. Cabanski, W. Fick, R. Koch, J. Ziegler, and S. D. Gunapala, “Medical infrared imaging – differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence,” Infrared Phys. Techn. 42, 337–344 (2001).
[Crossref]

2000 (1)

C. Benellii, E. Roscetti, and V. D. Pozzo, “The dermoscopic (7FFM) versus the clinical (ABCDE) diagnosis of small diameter melanoma,” Eur. J. Dermatol. 10, 282–287 (2000).
[PubMed]

1998 (1)

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

1995 (1)

A. D. Carlo, “Thermography and the possibilities for its applications in clinical and experimental dermatology,” Bioengineering of the Skin 13, 329–338 (1995).

1988 (1)

S. B. Wilson and V. A. Spence, “A tissue heat transfer model for relating dynamic skin temperatures changes to physiological parameters,” Phys. Med. Biol. 33, 895–912 (1988).
[Crossref] [PubMed]

1969 (1)

G. W. Bohrnstedt and A. S. Goldberger, “On the exact covariance of products of random variables,” J. Am. Stat. Assoc. 64, 1439–1442 (1969).
[Crossref]

1960 (1)

E. J. Kelly and W. L. Root, “A representation of vector-valued random processes,” J. Math. and Phys. 39, 211–216 (1960).
[Crossref]

1955 (1)

J. Pachares, “Note on the Distribution of a Definite Quadratic Form,” The Annals of Mathematical Statistics 26, 128–131 (1955).
[Crossref]

1950 (1)

U. Grenander, “Stochastic processes and statistical inference,” Arkiv för Matematik 1, 195–277 (1950).
[Crossref]

1948 (1)

H. Pennes, “Analysis of Tissue and Arterial Blood Temperature in the Resting Human Forearm,” J. Appl. Physiol. 1, 93–112 (1948).
[PubMed]

Abbasi, N. R.

N. R. Abbasi, H. M. Shaw, D. S. Rigel, R. J. Friedman, W. McCarthy, I. Osman, A. W. Kopf, and D. Polsky, “Early diagnosis of cutaneous melanoma: Revisiting the ABCDE criteria,” JAMA 292, 2771–2776 (2004).
[Crossref] [PubMed]

Acha, B.

A. Saez, C. Serrano, and B. Acha, “Model-based classification methods of global patterns in dermoscopic images,” IEEE Trans. Med. Imaging 33, 1137–1147 (2014).
[Crossref] [PubMed]

Ambar, M.

M. Ambar, Quantitative Dynamic Telethermometry in Medical Diagnosis and Management (CRC Press, Boca Raton, FL, 1994).

Argenziano, G.

G. Pellacani, B. D. Pace, C. Reggiani, A. M. Cesinaro, G. Argenziano, I. Zalaudek, H. P. Soyer, and C. Longo, “Distinct melanoma types based on reflectance confocal microscopy,” Experimental Dermatology 23, 414–418 (2014).
[Crossref] [PubMed]

Bagavathiappan, S.

B. B. Lahiri, S. Bagavathiappan, T. Jayakuman, and J. Phillip, “Medical applications of infrared thermography: A review,” Infrared Phys. Techn. 55, 221–235 (2012).
[Crossref]

Banzhaf, C.

L. Themstrup, C. Banzhaf, M. Mogensen, and G. Jemec, “Optical coherence tomography imaging of non-melanoma skin cancer undergoing photodynamic therapy reveals subclinical residual lesions,” Photodiagnosis Photodyn. Ther. 11, 7–12 (2014).
[Crossref]

Bard, R. L.

R. Kleinerman, T. B. Whang, R. L. Bard, and E. S. Marmur, “Ultrasound in dermatology: Principles and applications,” J. American Academy of Dermatology 67, 478–487 (2012).
[Crossref]

Bartoli, C.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Baurain, J. F.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Benellii, C.

C. Benellii, E. Roscetti, and V. D. Pozzo, “The dermoscopic (7FFM) versus the clinical (ABCDE) diagnosis of small diameter melanoma,” Eur. J. Dermatol. 10, 282–287 (2000).
[PubMed]

Berard, F.

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

Bohrnstedt, G. W.

G. W. Bohrnstedt and A. S. Goldberger, “On the exact covariance of products of random variables,” J. Am. Stat. Assoc. 64, 1439–1442 (1969).
[Crossref]

Boldo, E.

Bono, A.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Breiter, R.

M. A. Fauci, R. Breiter, W. Cabanski, W. Fick, R. Koch, J. Ziegler, and S. D. Gunapala, “Medical infrared imaging – differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence,” Infrared Phys. Techn. 42, 337–344 (2001).
[Crossref]

Buzug, T. M.

T. M. Buzug, S. Schumann, L. Pfaffmann, U. Reinhold, and J. Ruhlmann, Functional infrared imaging for skin-cancer screening, Conf. Proc. IEEE Eng. Med. Biol. Soc.1:2766–27692006.

Cabanski, W.

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E. J. Kelly and W. L. Root, “A representation of vector-valued random processes,” J. Math. and Phys. 39, 211–216 (1960).
[Crossref]

Roscetti, E.

C. Benellii, E. Roscetti, and V. D. Pozzo, “The dermoscopic (7FFM) versus the clinical (ABCDE) diagnosis of small diameter melanoma,” Eur. J. Dermatol. 10, 282–287 (2000).
[PubMed]

Roy, M.

D. S. Rigel, M. Roy, J. Yoo, C. J. Cockerell, J. K. Robinson, and R. White, “Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma,” Arch. Dermatol. 148, 541–543 (2012).
[Crossref] [PubMed]

Ruhlmann, J.

T. M. Buzug, S. Schumann, L. Pfaffmann, U. Reinhold, and J. Ruhlmann, Functional infrared imaging for skin-cancer screening, Conf. Proc. IEEE Eng. Med. Biol. Soc.1:2766–27692006.

Ruiz-Molina, J.

A. Oya, J. Navarro-Moreno, and J. Ruiz-Molina, “A numerical solution for multichannel detection,” IEEE Trans. Commun. pp. 1734–1742 (2009).
[Crossref]

Saez, A.

A. Saez, C. Serrano, and B. Acha, “Model-based classification methods of global patterns in dermoscopic images,” IEEE Trans. Med. Imaging 33, 1137–1147 (2014).
[Crossref] [PubMed]

Santinami, M.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Santoro, D. M. N.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Schumann, S.

T. M. Buzug, S. Schumann, L. Pfaffmann, U. Reinhold, and J. Ruhlmann, Functional infrared imaging for skin-cancer screening, Conf. Proc. IEEE Eng. Med. Biol. Soc.1:2766–27692006.

Schwartz, L. M.

H. G. Welch, S. Woloshin, and L. M. Schwartz, “Skin biopsy rates and incidence of melanoma: population based ecological study,” BMJ 331, 481 (2005).
[Crossref] [PubMed]

Secchi, T.

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

Sen, P.

S. E. Godoy, D. A. Ramirez, S. A. Myers, G. von Winckel, S. S. Krishna, R. S. Padilla, P. Sen, and S. Krishna, “Dynamic Infrared Imaging for Skin Cancer Screening,” Infrared Phys. Techn. 70, 147–152 (2015).
[Crossref]

Serov, A.

R. E. Hunger, R. D. Torre, A. Serov, and T. Hunziker, “Assessment of melanocytic skin lesions with a high-definition laser doppler imaging system,” Skin Res. Technol. 18, 207–211 (2012).
[Crossref]

Serrano, C.

A. Saez, C. Serrano, and B. Acha, “Model-based classification methods of global patterns in dermoscopic images,” IEEE Trans. Med. Imaging 33, 1137–1147 (2014).
[Crossref] [PubMed]

Shaw, H. M.

N. R. Abbasi, H. M. Shaw, D. S. Rigel, R. J. Friedman, W. McCarthy, I. Osman, A. W. Kopf, and D. Polsky, “Early diagnosis of cutaneous melanoma: Revisiting the ABCDE criteria,” JAMA 292, 2771–2776 (2004).
[Crossref] [PubMed]

Shi, G.

Sotoca, J. M.

Soyer, H. P.

G. Pellacani, B. D. Pace, C. Reggiani, A. M. Cesinaro, G. Argenziano, I. Zalaudek, H. P. Soyer, and C. Longo, “Distinct melanoma types based on reflectance confocal microscopy,” Experimental Dermatology 23, 414–418 (2014).
[Crossref] [PubMed]

Spence, V. A.

S. B. Wilson and V. A. Spence, “A tissue heat transfer model for relating dynamic skin temperatures changes to physiological parameters,” Phys. Med. Biol. 33, 895–912 (1988).
[Crossref] [PubMed]

Speybroeck, N.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Stephens, M.

C. Harris and M. Stephens, “A combined corner and edge detector,” in “In Proceedings of the Fourth Alvey Vision Conference,” (1988), pp. 147–151.

Swetter, S. M.

J. E. Mayer, S. M. Swetter, T. Fu, and A. C. Geller, “Screening, early detection, education, and trends for melanoma: Current status (2007–2013) and future directions: Part II. Screening, education, and future directions,” Journal of the American Academy of Dermatology 71, 611 (2014).
[Crossref]

Theate, I.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Themstrup, L.

L. Themstrup, C. Banzhaf, M. Mogensen, and G. Jemec, “Optical coherence tomography imaging of non-melanoma skin cancer undergoing photodynamic therapy reveals subclinical residual lesions,” Photodiagnosis Photodyn. Ther. 11, 7–12 (2014).
[Crossref]

Thomas, L.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

Toledano, A.

R. Wells, D. Gutkowicz-Krusin, E. Veledar, A. Toledano, and S. C. Chen, “Comparison of diagnostic and management sensitivity to melanoma between dermatologists and melafind: A pilot study,”, Arch. Dermatol. 148, 1083–1084 (2012).
[Crossref] [PubMed]

Tolomio, E.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Tomatis, S.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Torre, R. D.

R. E. Hunger, R. D. Torre, A. Serov, and T. Hunziker, “Assessment of melanocytic skin lesions with a high-definition laser doppler imaging system,” Skin Res. Technol. 18, 207–211 (2012).
[Crossref]

Torres, S. N.

Tragni, G.

M. Carrara, A. Bono, C. Bartoli, A. Colombo, M. Lualdi, D. M. N. Santoro, E. Tolomio, S. Tomatis, G. Tragni, M. Santinami, and R. Marchesini, “Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions,” Phys. Med. Biol. 52, 2599–2613 (2007).
[Crossref] [PubMed]

Tranchand, P.

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

Trees, H. L. V.

H. L. V. Trees, Detection, Estimation and Modulation Theory, Part 1, 2nd ed. (John Wiley & Son, Inc., 2001).

Trkov, A.

G. Zerovnik, A. Trkov, and I. A. Kodeli, “Correlated random sampling for multivariate normal and log-normal distributions,” Nucl. Instrum. Methods Phys. Res. A 690, 75–78 (2012).
[Crossref]

Tromme, L. S. I.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Upile, T.

Z. Hamdoon, W. Jerjes, T. Upile, and C. Hopper, “Optical coherence tomography-guided photodynamic therapy for skin cancer: Case study,” Photodiagnosis Photodyn. Ther. 8, 49–52 (2011).
[Crossref] [PubMed]

van Eeckhout, P.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Veledar, E.

R. Wells, D. Gutkowicz-Krusin, E. Veledar, A. Toledano, and S. C. Chen, “Comparison of diagnostic and management sensitivity to melanoma between dermatologists and melafind: A pilot study,”, Arch. Dermatol. 148, 1083–1084 (2012).
[Crossref] [PubMed]

Vereecken, P.

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

von Winckel, G.

S. E. Godoy, D. A. Ramirez, S. A. Myers, G. von Winckel, S. S. Krishna, R. S. Padilla, P. Sen, and S. Krishna, “Dynamic Infrared Imaging for Skin Cancer Screening,” Infrared Phys. Techn. 70, 147–152 (2015).
[Crossref]

Wang, L.

Weinstock, M. A.

H. W. Rogers, M. A. Weinstock, A. R. Harris, M. R. Hinckley, S. R. Feldman, A. B. Fleischer, and B. M. Coldiron, “Incidence estimate of nonmelanoma skin cancer in the United States, 2006,” Arch. Dermatol. 146, 283–287 (2010).
[Crossref] [PubMed]

Welch, H. G.

H. G. Welch, S. Woloshin, and L. M. Schwartz, “Skin biopsy rates and incidence of melanoma: population based ecological study,” BMJ 331, 481 (2005).
[Crossref] [PubMed]

Wells, R.

R. Wells, D. Gutkowicz-Krusin, E. Veledar, A. Toledano, and S. C. Chen, “Comparison of diagnostic and management sensitivity to melanoma between dermatologists and melafind: A pilot study,”, Arch. Dermatol. 148, 1083–1084 (2012).
[Crossref] [PubMed]

Whang, T. B.

R. Kleinerman, T. B. Whang, R. L. Bard, and E. S. Marmur, “Ultrasound in dermatology: Principles and applications,” J. American Academy of Dermatology 67, 478–487 (2012).
[Crossref]

White, R.

D. S. Rigel, M. Roy, J. Yoo, C. J. Cockerell, J. K. Robinson, and R. White, “Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma,” Arch. Dermatol. 148, 541–543 (2012).
[Crossref] [PubMed]

Wilson, S. B.

S. B. Wilson and V. A. Spence, “A tissue heat transfer model for relating dynamic skin temperatures changes to physiological parameters,” Phys. Med. Biol. 33, 895–912 (1988).
[Crossref] [PubMed]

Woloshin, S.

H. G. Welch, S. Woloshin, and L. M. Schwartz, “Skin biopsy rates and incidence of melanoma: population based ecological study,” BMJ 331, 481 (2005).
[Crossref] [PubMed]

Yang, M.

M. Yang, “Normal Log-normal Mixture: Leptokurtosis, Skewness and Applications,” Appl. Econ. Lett. 15, 737–742 (2008).
[Crossref]

Yano, H.

N. Nishida, H. Yano, T. Nishida, T. Kamura, and M. Kojiro, “Angiogenesis in Cancer,” Vascular Health and Risk Management 2, 213–219 (2006).
[Crossref]

Yoo, J.

D. S. Rigel, M. Roy, J. Yoo, C. J. Cockerell, J. K. Robinson, and R. White, “Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma,” Arch. Dermatol. 148, 541–543 (2012).
[Crossref] [PubMed]

Zalaudek, I.

G. Pellacani, B. D. Pace, C. Reggiani, A. M. Cesinaro, G. Argenziano, I. Zalaudek, H. P. Soyer, and C. Longo, “Distinct melanoma types based on reflectance confocal microscopy,” Experimental Dermatology 23, 414–418 (2014).
[Crossref] [PubMed]

Zerovnik, G.

G. Zerovnik, A. Trkov, and I. A. Kodeli, “Correlated random sampling for multivariate normal and log-normal distributions,” Nucl. Instrum. Methods Phys. Res. A 690, 75–78 (2012).
[Crossref]

Ziegler, J.

M. A. Fauci, R. Breiter, W. Cabanski, W. Fick, R. Koch, J. Ziegler, and S. D. Gunapala, “Medical infrared imaging – differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence,” Infrared Phys. Techn. 42, 337–344 (2001).
[Crossref]

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M. Yang, “Normal Log-normal Mixture: Leptokurtosis, Skewness and Applications,” Appl. Econ. Lett. 15, 737–742 (2008).
[Crossref]

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D. S. Rigel, M. Roy, J. Yoo, C. J. Cockerell, J. K. Robinson, and R. White, “Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma,” Arch. Dermatol. 148, 541–543 (2012).
[Crossref] [PubMed]

H. W. Rogers, M. A. Weinstock, A. R. Harris, M. R. Hinckley, S. R. Feldman, A. B. Fleischer, and B. M. Coldiron, “Incidence estimate of nonmelanoma skin cancer in the United States, 2006,” Arch. Dermatol. 146, 283–287 (2010).
[Crossref] [PubMed]

R. Wells, D. Gutkowicz-Krusin, E. Veledar, A. Toledano, and S. C. Chen, “Comparison of diagnostic and management sensitivity to melanoma between dermatologists and melafind: A pilot study,”, Arch. Dermatol. 148, 1083–1084 (2012).
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H. G. Welch, S. Woloshin, and L. M. Schwartz, “Skin biopsy rates and incidence of melanoma: population based ecological study,” BMJ 331, 481 (2005).
[Crossref] [PubMed]

Br. J. Dermatol. (1)

L. S. I. Tromme, F. Hammouch, C. Legrand, L. Marot, P. Vereecken, I. Theate, P. van Eeckhout, P. Richez, J. F. Baurain, L. Thomas, and N. Speybroeck, “Availability of digital dermoscopy in daily practice dramatically reduces the number of excised melanocytic lesions: results from an observational study,” Br. J. Dermatol. 167, 778–786 (2012).
[Crossref] [PubMed]

Dermatology (1)

L. Thomas, P. Tranchand, F. Berard, T. Secchi, C. Colin, and G. Moulin, “Semiological Value of ABCDE Criteria in the Diagnosis of Cutaneous Pigmented Tumors,” Dermatology 197, 11–17 (1998).
[Crossref] [PubMed]

Eur. J. Dermatol. (1)

C. Benellii, E. Roscetti, and V. D. Pozzo, “The dermoscopic (7FFM) versus the clinical (ABCDE) diagnosis of small diameter melanoma,” Eur. J. Dermatol. 10, 282–287 (2000).
[PubMed]

Experimental Dermatology (1)

G. Pellacani, B. D. Pace, C. Reggiani, A. M. Cesinaro, G. Argenziano, I. Zalaudek, H. P. Soyer, and C. Longo, “Distinct melanoma types based on reflectance confocal microscopy,” Experimental Dermatology 23, 414–418 (2014).
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C. Herman, “The role of dynamic infrared imaging in melanoma diagnosis,” Expert Rev. Dermatol. 8, 177–184 (2013).
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IEEE Trans. Med. Imaging (1)

A. Saez, C. Serrano, and B. Acha, “Model-based classification methods of global patterns in dermoscopic images,” IEEE Trans. Med. Imaging 33, 1137–1147 (2014).
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S. E. Godoy, D. A. Ramirez, S. A. Myers, G. von Winckel, S. S. Krishna, R. S. Padilla, P. Sen, and S. Krishna, “Dynamic Infrared Imaging for Skin Cancer Screening,” Infrared Phys. Techn. 70, 147–152 (2015).
[Crossref]

M. A. Fauci, R. Breiter, W. Cabanski, W. Fick, R. Koch, J. Ziegler, and S. D. Gunapala, “Medical infrared imaging – differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence,” Infrared Phys. Techn. 42, 337–344 (2001).
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R. Kleinerman, T. B. Whang, R. L. Bard, and E. S. Marmur, “Ultrasound in dermatology: Principles and applications,” J. American Academy of Dermatology 67, 478–487 (2012).
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A. Dancey, B. Mahon, and S. Rayatt, “A review of diagnostic imaging in melanoma,” J. Plast. Reconstr. Aesthet. Surg. 61, 1275–1283 (2008).
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JAMA (1)

N. R. Abbasi, H. M. Shaw, D. S. Rigel, R. J. Friedman, W. McCarthy, I. Osman, A. W. Kopf, and D. Polsky, “Early diagnosis of cutaneous melanoma: Revisiting the ABCDE criteria,” JAMA 292, 2771–2776 (2004).
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A. R. Cukras, “On the comparison of diagnosis and management of melanoma between dermatologists and melafind,” JAMA Dermatology 149, 622–623 (2013).
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Journal of the American Academy of Dermatology (1)

J. E. Mayer, S. M. Swetter, T. Fu, and A. C. Geller, “Screening, early detection, education, and trends for melanoma: Current status (2007–2013) and future directions: Part II. Screening, education, and future directions,” Journal of the American Academy of Dermatology 71, 611 (2014).
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M. P. Cetingul and C. Herman, “The Assessment of Melanoma Risk Using the Dynamic Infrared Imaging Technique,” Journal of Thermal Science and Engineering Applications 3, 031006 (2011).
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G. Zerovnik, A. Trkov, and I. A. Kodeli, “Correlated random sampling for multivariate normal and log-normal distributions,” Nucl. Instrum. Methods Phys. Res. A 690, 75–78 (2012).
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Photodiagnosis Photodyn. Ther. (2)

Z. Hamdoon, W. Jerjes, T. Upile, and C. Hopper, “Optical coherence tomography-guided photodynamic therapy for skin cancer: Case study,” Photodiagnosis Photodyn. Ther. 8, 49–52 (2011).
[Crossref] [PubMed]

L. Themstrup, C. Banzhaf, M. Mogensen, and G. Jemec, “Optical coherence tomography imaging of non-melanoma skin cancer undergoing photodynamic therapy reveals subclinical residual lesions,” Photodiagnosis Photodyn. Ther. 11, 7–12 (2014).
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Figures (9)

Fig. 1
Fig. 1

Tumor angiogenesis in cancer at different stages: (a) The tumor release growth factors that activate the growing cells generating blood vessel sprouts. (b) The blood vessels feed the tumor that growths thanks to cell proliferation. (c) The tumor becomes vascularized and it starts to metastasize through the blood stream (from webpage [33]).

Fig. 2
Fig. 2

(a) Auto-covariance function for the null-hypothesis (H0) estimated from patient data with known benign condition. (b) Auto-covariance function for the alternative-hypothesis (H1) estimated from patient data with known malignant condition. In order to highlight their differences, (c) and (d) show the projection onto one of the left plane of the Auto-covariance function for the null-hypothesis and the alternative-hypothesis, respectively.

Fig. 3
Fig. 3

False-alarm and detection probabilities parameterized by the threshold value, η, for different number of eigenfunctions used in the construction of the test-statistic (14)

Fig. 4
Fig. 4

The theoretical receiver-operating characteristic (ROC) curve graphically shows the expected performance of the detector as we increase the number of eigenvalue-eigenfunction pairs. The larger the number of the pairs utilized to construct the test-statistic, the more statistical features utilized and the better the performance of the algorithm

Fig. 5
Fig. 5

Block diagram of the detection stage of the proposed algorithm. The KL coefficients are computed by using the eigenfunctions of each hypothesis. These coefficients and the eigenvalues are used to compute the patient’s test-statistic, which is later compared with the optimum threshold to declare the malignancy

Fig. 6
Fig. 6

ACFs for the case of vectorial random processes: (a) Autocorrelation function for the null-hypothesis (H0) estimated from patient data with known benign condition. (b) Autocorrelation function for the alternative-hypothesis (H1) estimated from patient data with known malignant condition.

Fig. 7
Fig. 7

Acquisition hardware utilized to acquire the patient datasets. (a) Prototype and (b) Infrared imager and aquisition software

Fig. 8
Fig. 8

Example of a patient dataset: (a) example of one square plastic marker used in the data acquisition step; (b) first frame of the infrared sequence, note that the visible and this frame are spatially aligned; and (c) the thermal recovery curves (TRCs) for the labeled pixels in (b).

Fig. 9
Fig. 9

Comparison of the mean theoretical ROC curves over 200 permutations when 110 training are used to train the single-TRC algorithm (blue) and the dual-TRC algorithm (red). Comparison is made by using the mean AUC for different number of used eigenvalue-eigenfunction pairs, using 110 patients to train the algorithm.

Tables (1)

Tables Icon

Table 1 Comparison between the proposed methodology and other non-invasive techniques

Equations (36)

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s t = D 2 s x 2 , t [ 0 , T ] x [ 0 , H ] ,
s ( 0 , t ) = ( T B h H k T A 1 h k H ) + n = 1 C n exp ( D μ n 2 t ) , t [ 0 , T ] .
S ( t ; Θ j ) = θ j , 1 + θ j , 2 exp ( θ j , 3 t ) + θ j , 4 exp ( θ j , 5 t ) + N ( t ) ,
H 0 : Y ( t ) = S ( t ; Θ 0 ) , t [ 0 , T ]
H 1 : Y ( t ) = S ( t ; Θ 1 ) , t [ 0 , T ]
C j ( t , u ) = E [ θ j , 1   2 ] + E [ θ j , 1 θ j , 2 exp ( θ j , 3 u ) ] + E [ θ j , 1 θ j , 4 exp ( θ j , 5 u ) ] + E [ θ j , 1 θ j , 2 exp ( θ j , 3 t ) ] + E [ θ j , 2   2 exp ( θ j , 3 ( t + u ) ) ] + E [ θ j , 2 θ j , 4 exp ( θ j , 3 t ) exp ( θ j , 5 u ) ] + E [ θ j , 1 θ j , 4 exp ( θ j , 5 t ) ] + E [ θ j , 2 θ j , 4 exp ( θ j , 5 t ) exp ( θ j , 3 u ) ] + E [ θ j , 4 2 exp ( θ j , 5 ( t + u ) ) ] , + E [ N 2 ( t ) ] E [ S ( t ; Θ j ) ] E [ S ( u ; Θ j ) ]
E [ θ j , n θ j , m exp ( θ j , t ) ] = E [ θ j , n ] E [ θ j , m ] E [ exp ( θ j , t ) ] + E [ θ j , n ] cov ( θ j , m , exp ( θ j , t ) ) + E [ θ j , m ] cov ( θ j , n , exp ( θ j , t ) ) + E [ exp ( θ j , t ) ] cov ( θ j , n , θ j , m ) .
cov ( θ j , n , exp ( θ j , t ) ) = ρ n , σ j , n σ j , t exp ( t μ j , + t 2 σ j , 2 2 ) ,
E [ θ j , n θ j , m exp ( θ j , t ) ] = ( cov ( θ j , n , θ j , m ) + E [ θ j , n ] E [ θ j , m ] ) exp ( θ j , t ) ,
C j ( t , u ) = k = 1 λ j , k ϕ j , k ( t ) ϕ j , k ( u ) , ( t , u ) [ 0 , T ] 2 ,
λ j , k ϕ j , k ( t ) = 0 T C j ( t , u ) ϕ j , k ( u ) d u , t [ 0 , T ] ,
S ( t ; Θ j ) = k = 1 S j , k ϕ j , k ( t ) , t [ ( 0 , T ) ] ,
S j , k = 0 T ϕ j , k ( t ) ( S ( t ; Θ j ) E [ S ( t ; Θ j ) ] ) d t , k = 1 , 2 , j = 0 , 1 ,
H 0 : S 0 , k , k = 1 , 2 ,
H 1 : S 1 , k , k = 1 , 2 ,
H 0 : Y 0 , k ~ N ( 0 , λ 0 , k ) , k = 1 , 2 ,
H 1 : Y 1 , k ~ N ( 0 , λ 1 , k ) , k = 1 , 2 ,
L ( Y ) p 1 ( Y ) p 0 ( Y ) = k = 1 ( λ 0 , k λ 1 , k ) 1 / 2 exp [ 1 2 k = 1 ( Y 0 , k 2 λ 0 , k Y 1 , k 2 λ 1 , k ) ] ,
Z = k = 1 ( Y 0 , k 2 λ 0 , k Y 1 , k 2 λ 1 , k ) > < η ,
Z 0 = k = 1 ( Y 0 , k 2 λ 0 , k Y 1 , k 2 λ 1 , k ) = k = 1 ( S 0 , k 2 λ 0 , k S 1 , k 2 λ 1 , k ) = k = 1 ( 1 λ 0 , k λ 1 , k ) S 0 , k 2 λ 0 , k .
Z 0 = 1 2 k 1 a k X k ,
Z 1 = k = 1 ( S 1 , k 2 λ 0 , k S 1 , k 2 λ 1 , k ) = k = 1 ( λ 1 , k λ 0 , k 1 ) S 1 , k 2 λ 1 , k = 1 2 k = 1 b k X k ,
G ( τ ; c ) = Pr ( Z j τ ) = τ K c 1 c 2 c K k = 0 ( τ ) k k ! E [ Z j * ] Γ ( M / 2 + k + 1 ) ,
P F Pr ( L ( Y ) > τ | H 0 ) = Pr ( Z 0 > η ) = 1 Pr ( Z 0 η ) = 1 G ( η ; a ) ,
η 0 = G 1 ( 1 α ; a ) ,
X ( t ; Θ j ) = [ S ( t ; Θ S , j ) T ( t ; Θ T , j ) ] , t [ 0 , T ] ,
H 0 : Y ( t ) = X ( t ; Θ 0 ) , t [ 0 , T ] ,
H 1 : Y ( t ) = X ( t ; Θ 1 ) , t [ 0 , T ] .
R j ( t , u ) E [ X ( t ; Θ j ) X T ( u ; Θ j ) ] = [ E E [ S ( t ; Θ S , j ) S ( t ; Θ S , j ) ] E [ S ( t ; Θ S , j ) T ( t ; Θ T , j ) ] [ T ( t ; Θ T , j ) S ( t ; Θ S , j ) ] E [ T ( t ; Θ T , j ) T ( t ; Θ T , j ) ] ] .
X ( t ; Θ j ) = k = 1 X k Φ j , k ( t ) .
X j , k 0 T Φ j , k T ( t ) X ( t ; Θ j ) d t = 0 T X T ( t ; Θ j ) Φ j , k ( t ) d t .
0 T R j ( t , u ) Φ j , ( u ) d u d u = λ j , Φ j , ( t ) ,
R j ( t , u ) = E [ X ( t ; Θ j ) X T ( u ; Θ j ) ] = k = 1 λ j , k Φ j , k ( t ) Φ j , k T ( u ) .
X ( t ; Θ j ) = k = 1 X j , k Φ j , k ( t ) , t [ 0 , T ] , j = 0 , 1 .
H 0 : X 0 , k , k = 1 , 2 ,
H 1 : X 1 , k , k = 1 , 2 , ,

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