Abstract

A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.

© 2013 OSA

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2012 (1)

2007 (1)

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

2003 (2)

J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: A survey,” IEEE Trans. Med. Imaging22(8), 986–1004 (2003).
[CrossRef] [PubMed]

I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res.3, 1157–1182 (2003).

2002 (1)

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).

2001 (2)

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

S. Kumar, J. Ghosh, and M. Crawford, “Best-bases feature extraction algorithms for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens.39, 1368–1379 (2001).
[CrossRef]

1997 (1)

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn.20, 273–297 (1995).
[CrossRef]

Bartoli, C.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Bogdan, A.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Boldó, E.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Bono, A.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Bowyer, K. W.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).

Carrara, M.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Chawla, N. V.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).

Collignon, A.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

Colombo, A.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Cortes, C.

C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn.20, 273–297 (1995).
[CrossRef]

Cover, T. M.

T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley And Sons, 1991).
[CrossRef]

Crawford, M.

S. Kumar, J. Ghosh, and M. Crawford, “Best-bases feature extraction algorithms for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens.39, 1368–1379 (2001).
[CrossRef]

D‘Alessandro, B.

B. D‘Alessandro and A. P. Dhawan, “Blood Oxygen Saturation Estimation in Transilluminated Images of Skin Lesions,” in Proceedings of the IEEE-EMBS on BHI (2012), pp. 729–732.

A. P. Dhawan, B. D‘Alessandro, S. Patwardhan, and N. Mullani, “Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant Melanomas,” in Proceedings of IEEE EMBS (2009), pp. 5352–5255.

B. D‘Alessandro and A. P. Dhawan, “Multispectral Transillumination Imaging of Skin Lesions for Oxygenated and Deoxygenated Hemoglobin Measurement,” in Proceedings of IEEE EMBS (2010), pp. 6637–6640.

Derjabo, A.

Dhawan, A. P.

B. D‘Alessandro and A. P. Dhawan, “Multispectral Transillumination Imaging of Skin Lesions for Oxygenated and Deoxygenated Hemoglobin Measurement,” in Proceedings of IEEE EMBS (2010), pp. 6637–6640.

A. P. Dhawan, B. D‘Alessandro, S. Patwardhan, and N. Mullani, “Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant Melanomas,” in Proceedings of IEEE EMBS (2009), pp. 5352–5255.

B. D‘Alessandro and A. P. Dhawan, “Blood Oxygen Saturation Estimation in Transilluminated Images of Skin Lesions,” in Proceedings of the IEEE-EMBS on BHI (2012), pp. 729–732.

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Wavelength Selection for Multi-Spectral Imaging of Skin Lesions Using Nevoscope,” in Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference (2003), pp. 327–328.
[CrossRef]

Diebele, I.

Elbaum, M.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Elisseeff, A.

I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res.3, 1157–1182 (2003).

Ferri, F. J.

P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, “Floating search methods for feature selection with nonmonotonic criterion functions,” Proc. of the 12th Int. Conf. on Pat. Rec. (1994), Vol. 2, pp. 279–283.

García, P.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

García, V.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Ghosh, J.

S. Kumar, J. Ghosh, and M. Crawford, “Best-bases feature extraction algorithms for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens.39, 1368–1379 (2001).
[CrossRef]

Greenebaum, M.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Gutkowicz-Krusin, D.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Guyon, I.

I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res.3, 1157–1182 (2003).

Hall, L. O.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).

Kamino, H.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Kapostinsh, J.

Keem, S.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Kegelmeyer, W. P.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).

Kittler, J.

P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, “Floating search methods for feature selection with nonmonotonic criterion functions,” Proc. of the 12th Int. Conf. on Pat. Rec. (1994), Vol. 2, pp. 279–283.

Kopf, A. W.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Kumar, S.

S. Kumar, J. Ghosh, and M. Crawford, “Best-bases feature extraction algorithms for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens.39, 1368–1379 (2001).
[CrossRef]

Kuzmina, I.

Langley, R. G. B.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Latorre Carmona, P.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Lihachev, A.

Lozoya, R.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Lualdi, M.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Maes, F.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

Maintz, J. B. A.

J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: A survey,” IEEE Trans. Med. Imaging22(8), 986–1004 (2003).
[CrossRef] [PubMed]

Marchal, G.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

Marchesini, R.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Mihm, M. C.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Mullani, N.

A. P. Dhawan, B. D‘Alessandro, S. Patwardhan, and N. Mullani, “Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant Melanomas,” in Proceedings of IEEE EMBS (2009), pp. 5352–5255.

Novovicova, J.

P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, “Floating search methods for feature selection with nonmonotonic criterion functions,” Proc. of the 12th Int. Conf. on Pat. Rec. (1994), Vol. 2, pp. 279–283.

Oliviero, M.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Patwardhan, S.

A. P. Dhawan, B. D‘Alessandro, S. Patwardhan, and N. Mullani, “Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant Melanomas,” in Proceedings of IEEE EMBS (2009), pp. 5352–5255.

Patwardhan, S. V.

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Wavelength Selection for Multi-Spectral Imaging of Skin Lesions Using Nevoscope,” in Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference (2003), pp. 327–328.
[CrossRef]

Peck, G. L.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Pérez de Lucía, G.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Pla, F.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Pluim, J. P. W.

J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: A survey,” IEEE Trans. Med. Imaging22(8), 986–1004 (2003).
[CrossRef] [PubMed]

Pudil, P.

P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, “Floating search methods for feature selection with nonmonotonic criterion functions,” Proc. of the 12th Int. Conf. on Pat. Rec. (1994), Vol. 2, pp. 279–283.

Quinzán, I.

I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.

Rabinovitz, H. S.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Relue, P. A.

S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Wavelength Selection for Multi-Spectral Imaging of Skin Lesions Using Nevoscope,” in Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference (2003), pp. 327–328.
[CrossRef]

Sober, A. J.

M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001).
[CrossRef] [PubMed]

Spigulis, J.

Suetens, P.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

Thomas, J. A.

T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley And Sons, 1991).
[CrossRef]

Tomatis, S.

R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007).
[PubMed]

Valeine, L.

Vandermeulen, D.

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997).
[CrossRef] [PubMed]

Vapnik, V.

C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn.20, 273–297 (1995).
[CrossRef]

Viergever, M. A.

J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: A survey,” IEEE Trans. Med. Imaging22(8), 986–1004 (2003).
[CrossRef] [PubMed]

Wang, S.

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Figures (2)

Fig. 1
Fig. 1

Multispectral image acquisition system.

Fig. 2
Fig. 2

Classification Model. Result for a sample x using bands in S.

Tables (1)

Tables Icon

Table 1 Results Obtained with Each Subset of Bands

Metrics