Abstract

This paper deals with multi-class classification of skin pre-cancerous stages based on bimodal spectroscopic features combining spatially resolved AutoFluorescence (AF) and Diffuse Reflectance (DR) measurements. A new hybrid method to extract and select features is presented. It is based on Discrete Cosine Transform (DCT) applied to AF spectra and on Mutual Information (MI) applied to DR spectra. The classification is performed by means of a multi-class SVM: the M-SVM2. Its performance is compared with the one of the One-Versus-All (OVA) decomposition method involving bi-class SVMs as base classifiers. The results of this study show that bimodality and the choice of an adequate spatial resolution allow for a significant increase in diagnostic accuracy. This accuracy can get as high as 81.7% when combining different distances in the case of bimodality.

© 2011 OSA

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  39. Y. Guermeur, “A generic model of multi-class support vector machine,” Int. J. Int. Inf. Datab. Sys. (2011), (accepted).
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    [CrossRef] [PubMed]
  44. M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
    [CrossRef] [PubMed]
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2011

Y. Guermeur and E. Monfrini., “A quadratic loss multi-class svm for which a radius-margin bound applies,” Informatica 22, 73–96 (2011).

F. Lauer and Y. Guermeur, “MSVMpack: a multi-class support vector machine package”, J. Mach. Lear. Res. 12, 2293–2296 (2011).

Y. Guermeur, “A generic model of multi-class support vector machine,” Int. J. Int. Inf. Datab. Sys. (2011), (accepted).

2010

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

2009

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

A. M. Sarhan, “Iris recognition using discrete cosine transform and artificial neural networks,” J. Comp. Scienc. 5, 369–373 (2009).
[CrossRef]

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

2008

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

E. Widjaja, W. Zheng, and Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol. 32, 653–662 (2008).
[PubMed]

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

2006

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

2005

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy,” Pat. Anal. Mach. Intel. 27, 1226–1238 (2005).
[CrossRef]

A. M. Pena, M. Strupler, and T. Boulesteix, “Spectroscopic analysis of keratin endogenous signal for skin multi-photon microscopy,” Opt. Express. 13, 6268–6274 (2005).
[CrossRef] [PubMed]

S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005).
[CrossRef] [PubMed]

2004

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

2003

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

F. Liang, “An effective bayesian neural network classifier with a comparison study to support vector machine,” Neural Comput. 15, 1959–1989 (2003).
[CrossRef]

2002

H. Chang and N. S. Kim, “Speech enhancement using warped discrete cosine transform,” Speech Coding. 175–177 (2002).

2001

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

2000

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

1999

M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer : In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer.  86, 2201–2211 (1999).
[CrossRef] [PubMed]

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

1998

G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

1997

P. K. Gupta, S. K. Majumder, and A. Uppal, “Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy,” Lasers Surg. Med. 21, 417–422 (1997).
[CrossRef] [PubMed]

1996

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

1995

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).

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

Amouroux, M.

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

Anderson, R. R.

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

Anthony, M.

M. Anthony and P. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, Cambridge, 1999).
[CrossRef]

Atkinson, N.

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Backhausa, J.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Bartlett, P.

M. Anthony and P. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, Cambridge, 1999).
[CrossRef]

Bianchi, A.

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

Blondel, W.

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

Blondel, W. C. P. M.

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Boulesteix, T.

A. M. Pena, M. Strupler, and T. Boulesteix, “Spectroscopic analysis of keratin endogenous signal for skin multi-photon microscopy,” Opt. Express. 13, 6268–6274 (2005).
[CrossRef] [PubMed]

Bourg-Heckly, G.

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

Breslin, T. M.

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

Bugertc, P.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Burges, C.

B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).

Cantor, S. B.

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

Cerutti, S.

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

Chang, H.

H. Chang and N. S. Kim, “Speech enhancement using warped discrete cosine transform,” Speech Coding. 175–177 (2002).

Cortes, C.

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

Cosatto, E.

G. Potamianos, H. P. Graf, and E. Cosatto, “An image transform approach for hmm based automatic lipreading,” IEEE Int. Conf. Image. Process. (1998).

Cover, T. M.

T. M. Cover and J. Thomas, Elements of information theory (Wiley Series in Telecommunications, New York, 1991).
[CrossRef]

Cristianini, N.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernelbased learning methods (Cambridge University Press, Cambridge, 2000).

D’Almeida, L.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Daul, C.

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Devroye, L.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer-Verlag, New York1996).

Dhingra, J.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Diaz-Ayil, G.

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

Didelon, J.

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

Ding, C.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy,” Pat. Anal. Mach. Intel. 27, 1226–1238 (2005).
[CrossRef]

Eidtb, M.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Feld, M.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Formanskia, N.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Ghosh, N.

S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005).
[CrossRef] [PubMed]

Gilchrist, K. W.

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

Gillies, R.

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

Graf, H. P.

G. Potamianos, H. P. Graf, and E. Cosatto, “An image transform approach for hmm based automatic lipreading,” IEEE Int. Conf. Image. Process. (1998).

Granjon, Y.

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

Guermeur, Y.

F. Lauer and Y. Guermeur, “MSVMpack: a multi-class support vector machine package”, J. Mach. Lear. Res. 12, 2293–2296 (2011).

Y. Guermeur and E. Monfrini., “A quadratic loss multi-class svm for which a radius-margin bound applies,” Informatica 22, 73–96 (2011).

Y. Guermeur, “A generic model of multi-class support vector machine,” Int. J. Int. Inf. Datab. Sys. (2011), (accepted).

Guillemin, F.

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Gupta, P.

S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005).
[CrossRef] [PubMed]

Gupta, P. K.

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

P. K. Gupta, S. K. Majumder, and A. Uppal, “Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy,” Lasers Surg. Med. 21, 417–422 (1997).
[CrossRef] [PubMed]

Györfi, L.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer-Verlag, New York1996).

Harries, M.

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

Harter, J.

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

Hashimoto, K.

M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer : In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer.  86, 2201–2211 (1999).
[CrossRef] [PubMed]

Hernandez-Mier, Y.

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Huang, Z.

E. Widjaja, W. Zheng, and Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol. 32, 653–662 (2008).
[PubMed]

Inaguma, M.

M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer : In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer.  86, 2201–2211 (1999).
[CrossRef] [PubMed]

Itzkan, I.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Jain, A.

A. Jain, Fundamentals of Digital Image Processing (Englewood Cliffs, NJ: Prentice-Hall1989).

Jain, B.

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

Jansen, E. D.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

Johnson, M.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

Kabani, S.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Kamath, S.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Kartha, V. B.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Keller, M. D.

M. D. Keller, Optical spectroscopy for the evaluation of surgical margin status following breast cancer resection, (Ph.D. dissertation, Nashville, Tennessee, 2009).
[PubMed]

Kim, N. S.

H. Chang and N. S. Kim, “Speech enhancement using warped discrete cosine transform,” Speech Coding. 175–177 (2002).

Kollias, N.

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

Krishnanand, B. R.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Kurien, J.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Lam, S.

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

Lauer, F.

F. Lauer and Y. Guermeur, “MSVMpack: a multi-class support vector machine package”, J. Mach. Lear. Res. 12, 2293–2296 (2011).

Leroux, A.

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

Liang, F.

F. Liang, “An effective bayesian neural network classifier with a comparison study to support vector machine,” Neural Comput. 15, 1959–1989 (2003).
[CrossRef]

Lin, W.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Lin, W. C.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

Long, F.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy,” Pat. Anal. Mach. Intel. 27, 1226–1238 (2005).
[CrossRef]

Lugosi, G.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer-Verlag, New York1996).

Macaulay, C.

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

Mahadevan, A.

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Mahadevan-Jansen, A.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

Mahato, K. K.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Majumder, S.

S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005).
[CrossRef] [PubMed]

Majumder, S. K.

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

P. K. Gupta, S. K. Majumder, and A. Uppal, “Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy,” Lasers Surg. Med. 21, 417–422 (1997).
[CrossRef] [PubMed]

Malpica, A.

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Manoharan, R.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Marcon, N.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

Matteucci, M.

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

McMillan, K.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Meerpohlb, H. G.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Mendez, M.

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

Miranda-Luna, R.

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Mitchel, I. J. L.

W. B. Pennebaker and I. J. L. Mitchel, Jpeg still image data compression standard (Van Nostrand Reinhold, New York, NY, 1993).

Mitchell, M. F.

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Molckovsky, A.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

Monfrini, E.

Y. Guermeur and E. Monfrini., “A quadratic loss multi-class svm for which a radius-margin bound applies,” Informatica 22, 73–96 (2011).

Motamedi, M.

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

Muellera, R.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Nayak, G. S.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Pai, K. M.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Palcic, B.

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

Palmer, G. M.

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

Pena, A. M.

A. M. Pena, M. Strupler, and T. Boulesteix, “Spectroscopic analysis of keratin endogenous signal for skin multi-photon microscopy,” Opt. Express. 13, 6268–6274 (2005).
[CrossRef] [PubMed]

Peng, H.

H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy,” Pat. Anal. Mach. Intel. 27, 1226–1238 (2005).
[CrossRef]

Pennebaker, W. B.

W. B. Pennebaker and I. J. L. Mitchel, Jpeg still image data compression standard (Van Nostrand Reinhold, New York, NY, 1993).

Penzel, T.

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

Perrault, D.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Pery, E.

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

Potamianos, G.

G. Potamianos, H. P. Graf, and E. Cosatto, “An image transform approach for hmm based automatic lipreading,” IEEE Int. Conf. Image. Process. (1998).

Qu, J.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

Ramanujam, N.

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Ray, S.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Rebeiz, E.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Richards-Kortum, R.

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Roodenburgb, J. L. N.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

Santhosh, C.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Sarhan, A. M.

A. M. Sarhan, “Iris recognition using discrete cosine transform and artificial neural networks,” J. Comp. Scienc. 5, 369–373 (2009).
[CrossRef]

Sarkar, A.

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Schölkopf, B.

B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).

Sham, J.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Shapshay, S. M.

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Shawe-Taylor, J.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernelbased learning methods (Cambridge University Press, Cambridge, 2000).

Shi, P. C.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Shim, M.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

Speelman, O. C.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

Star, W.

G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

Star, W. M.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

Strupler, M.

A. M. Pena, M. Strupler, and T. Boulesteix, “Spectroscopic analysis of keratin endogenous signal for skin multi-photon microscopy,” Opt. Express. 13, 6268–6274 (2005).
[CrossRef] [PubMed]

Szlamaa, N.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Thomas, J.

T. M. Cover and J. Thomas, Elements of information theory (Wiley Series in Telecommunications, New York, 1991).
[CrossRef]

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N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Toms, S. A.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

Tortolero-Luna, G.

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

Uppal, A.

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

P. K. Gupta, S. K. Majumder, and A. Uppal, “Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy,” Lasers Surg. Med. 21, 417–422 (1997).
[CrossRef] [PubMed]

van Staveren, H. J.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

van Veen, R. L. P.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

Vapnik, V.

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

B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).

V. Vapnik, The nature of statistical learning theory (Springer-Verlag, New York, 1995).

Wagnieres, G.

G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

Watkins, C.

J. Weston and C. Watkins, Multi-class support vector machines, (Technical Report CSD-TR- 98-04, Royal Holloway, University of London, Department of Computer Science, 1998).

Wei, W. I.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Weston, J.

J. Weston and C. Watkins, Multi-class support vector machines, (Technical Report CSD-TR- 98-04, Royal Holloway, University of London, Department of Computer Science, 1998).

Widjaja, E.

E. Widjaja, W. Zheng, and Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol. 32, 653–662 (2008).
[PubMed]

Wilson, B.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

Witjes, M. J. H.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

Wolf, D.

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

Wong, K.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

Wright, T.

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Xu, F.

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

Yuan, X.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Yuen, P.

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

Zheng, W.

E. Widjaja, W. Zheng, and Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol. 32, 653–662 (2008).
[PubMed]

Zhu, C.

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

Zonios, G.

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

Archives of Otolaryngology-Head and Neck Surgery

J. Dhingra, D. Perrault, K. McMillan, E. Rebeiz, S. Kabani, R. Manoharan, I. Itzkan, M. Feld, and S. M. Shapshay, “Early diagnosis of upper aerodigestive tract cancer by autofluorescence,” Archives of Otolaryngology-Head and Neck Surgery.  122, 1181–6 (1996).
[CrossRef] [PubMed]

Biopolymers

G. S. Nayak, S. Kamath, K. M. Pai, A. Sarkar, S. Ray, J. Kurien, L. D’Almeida, B. R. Krishnanand, C. Santhosh, V. B. Kartha, and K. K. Mahato, “Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra : classification of normal premalignant and malignant pathological conditions,” Biopolymers 82, 152–166 (2006).
[CrossRef] [PubMed]

Cancer

M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer : In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer.  86, 2201–2211 (1999).
[CrossRef] [PubMed]

Europ. Physic. J. App. Physic.

G. Diaz-Ayil, M. Amouroux, W. Blondel, G. Bourg-Heckly, A. Leroux, F. Guillemin, and Y. Granjon, “Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM),” Europ. Physic. J. App. Physic. 4712707–718 (2009).

Gastrointest. Endosc.

A. Molckovsky, K. Wong, M. Shim, N. Marcon, and B. Wilson, “Diagnostic potential of nearinfrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps,” Gastrointest. Endosc. 57, 396–402 (2003).
[CrossRef] [PubMed]

IEEE Trans. Biomed. Eng.

G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer,” IEEE Trans. Biomed. Eng. 50, 1233–1242 (2003).
[CrossRef] [PubMed]

E. Pery, W. Blondel, J. Didelon, A. Leroux, and F. Guillemin, “Simultaneous characterization of optical and rheological properties of carotid arteries via bimodal spectroscopy: Experimental and simulation results,” IEEE Trans. Biomed. Eng. 56, 1267–1276 (2009).
[CrossRef] [PubMed]

R. Miranda-Luna, C. Daul, W. C. P. M. Blondel, Y. Hernandez-Mier, D. Wolf, and F. Guillemin, “Mosaicing of bladder endoscopic image sequences: Distortion calibration and registration algorithm,” IEEE Trans. Biomed. Eng. 55, 541–553 (2008).
[CrossRef] [PubMed]

M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening by autoregressive models from a single ecg lead,” IEEE Trans. Biomed. Eng. 56, 2838–2850 (2009).
[CrossRef] [PubMed]

Informatica

Y. Guermeur and E. Monfrini., “A quadratic loss multi-class svm for which a radius-margin bound applies,” Informatica 22, 73–96 (2011).

Int. Conf. Knowledge Discov. Data. Mining

B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).

Int. J. Int. Inf. Datab. Sys.

Y. Guermeur, “A generic model of multi-class support vector machine,” Int. J. Int. Inf. Datab. Sys. (2011), (accepted).

Int. J. Oncol.

E. Widjaja, W. Zheng, and Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol. 32, 653–662 (2008).
[PubMed]

J. Biomed. Opt.

W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy; an in vitro study,” J. Biomed. Opt. 5, 214–220 (2000).
[CrossRef] [PubMed]

S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005).
[CrossRef] [PubMed]

M. Amouroux, G. Diaz-Ayil, W. Blondel, G. Bourg-Heckly, A. Leroux, and F. Guillemin, “Classification of ultraviolet irradiated mouse skin histological stages by bimodal spectroscopy (multiple excitation autofluorescence and diffuse reflectance),” J. Biomed. Opt. 14, 14 011–14 024 (2009).
[CrossRef]

W. Lin, X. Yuan, P. Yuen, W. I. Wei, J. Sham, P. C. Shi, and J. Qu, “Classification of in vivo autofluorescence spectra using support vector machines,” J. Biomed. Opt. 9, 180–186 (2004).
[CrossRef] [PubMed]

J. Comp. Scienc.

A. M. Sarhan, “Iris recognition using discrete cosine transform and artificial neural networks,” J. Comp. Scienc. 5, 369–373 (2009).
[CrossRef]

J. Invest. Dermatol.

R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, “Fluorescence excitation spectroscopy provides information about human skin in vivo,” J. Invest. Dermatol. 115, 704–707 (2000).
[CrossRef] [PubMed]

J. Laryngol. Otol.

M. Harries, S. Lam, C. Macaulay, J. Qu, and B. Palcic, “Diagnostic imaging of the larynx: autofluorescence of laryngeal tumours using the helium-cadmium laser,” J. Laryngol. Otol. 109, 108–110 (1995).
[CrossRef] [PubMed]

J. Mach. Lear. Res.

F. Lauer and Y. Guermeur, “MSVMpack: a multi-class support vector machine package”, J. Mach. Lear. Res. 12, 2293–2296 (2011).

Lasers in the Life Sciences

S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, “UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue,” Lasers in the Life Sciences 8, 249–264 (1999).

Lasers Surg. Med.

P. K. Gupta, S. K. Majumder, and A. Uppal, “Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy,” Lasers Surg. Med. 21, 417–422 (1997).
[CrossRef] [PubMed]

N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, “Spectroscopic diagnosis of cervical intraepithelial neoplasia (cin) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths,” Lasers Surg. Med. 19, 63–74 (1996).
[CrossRef] [PubMed]

Mach. Lear.

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

Neural Comput.

F. Liang, “An effective bayesian neural network classifier with a comparison study to support vector machine,” Neural Comput. 15, 1959–1989 (2003).
[CrossRef]

Obstet. Gynecol.

M. F. Mitchell, S. B. Cantor, N. Ramanujam, G. Tortolero-Luna, and R. Richards-Kortum, “Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix,” Obstet. Gynecol. 93, 462–470 (1999).
[CrossRef] [PubMed]

Opt. Express.

C. Zhu, T. M. Breslin, J. Harter, and N. Ramanujam, “Model based and empirical spectral analysis for the diagnosis of breast cancer,” Opt. Express. 1614961–978 (2008).
[CrossRef]

A. M. Pena, M. Strupler, and T. Boulesteix, “Spectroscopic analysis of keratin endogenous signal for skin multi-photon microscopy,” Opt. Express. 13, 6268–6274 (2005).
[CrossRef] [PubMed]

Oral Oncol.

H. J. van Staveren, R. L. P. van Veen, O. C. Speelman, M. J. H. Witjes, W. M. Star, and J. L. N. Roodenburgb, “Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study,” Oral Oncol. 36, 286–293 (2000).
[CrossRef] [PubMed]

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H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy,” Pat. Anal. Mach. Intel. 27, 1226–1238 (2005).
[CrossRef]

Phot. Chem. Photobiol.

G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

Photochem Photobiol.

W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem Photobiol. 73, 396–402 (2001).
[CrossRef] [PubMed]

Speech Coding

H. Chang and N. S. Kim, “Speech enhancement using warped discrete cosine transform,” Speech Coding. 175–177 (2002).

Vibrat. Spect.

J. Backhausa, R. Muellera, N. Formanskia, N. Szlamaa, H. G. Meerpohlb, M. Eidtb, and P. Bugertc, “Diagnosis of breast cancer with infrared spectroscopy from serum samples,” Vibrat. Spect. 52, 173–177 (2010).
[CrossRef]

Other

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernelbased learning methods (Cambridge University Press, Cambridge, 2000).

A. Jain, Fundamentals of Digital Image Processing (Englewood Cliffs, NJ: Prentice-Hall1989).

T. M. Cover and J. Thomas, Elements of information theory (Wiley Series in Telecommunications, New York, 1991).
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W. B. Pennebaker and I. J. L. Mitchel, Jpeg still image data compression standard (Van Nostrand Reinhold, New York, NY, 1993).

G. Potamianos, H. P. Graf, and E. Cosatto, “An image transform approach for hmm based automatic lipreading,” IEEE Int. Conf. Image. Process. (1998).

J. Weston and C. Watkins, Multi-class support vector machines, (Technical Report CSD-TR- 98-04, Royal Holloway, University of London, Department of Computer Science, 1998).

V. Vapnik, The nature of statistical learning theory (Springer-Verlag, New York, 1995).

M. Anthony and P. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, Cambridge, 1999).
[CrossRef]

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer-Verlag, New York1996).

M. D. Keller, Optical spectroscopy for the evaluation of surgical margin status following breast cancer resection, (Ph.D. dissertation, Nashville, Tennessee, 2009).
[PubMed]

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Figures (8)

Fig. 1
Fig. 1

Examples of emission autofluorescence spectrum for an excitation wavelength of 360 nm (a) and diffuse reflectance spectrum (c), both acquired at interfiber distance D1. (b) and (d) show the 20 first DCT coefficients calculated for (a) and (c) respectively.

Fig. 2
Fig. 2

Examples of 7 AF spectra concatenated end-to-end (a), and an DR spectrum (b). Occurrence histograms of the discriminant features selected with MI for the AF spectra (c) and the DR spectrum (d).

Fig. 3
Fig. 3

Recognition rate τ(%) calculated as a function of the number of DCT coefficients retained for (left) AF spectra alone acquired at D1, (center) DR spectra acquired at D1, (right) AF and DR spectra all acquired at D1 (bimodality).

Fig. 4
Fig. 4

Recognition rate τ(%) as a function of the number of features selected with MI for (a) AF spectra and (b) for DR spectra acquired at D1.

Fig. 5
Fig. 5

Recognition rate as a function of the number of DCT coefficients for the bimodal configuration (AF+DR) for the 3 CEFS (a) D1 (b) D2 (c) D3.

Fig. 6
Fig. 6

Recognition rate of the M-SVM2 as a function of the 7 combinations of (Di)1⩽i⩽3 distances and the 3 modalities: AF alone (black bar), DR alone (grey bar) and AF+DR together (light grey bar), using (left) DCT method, (center) MI method and (right) hybrid method.

Fig. 7
Fig. 7

Recognition rate as a function of the number of DCT coefficients calculated for each of the 7 AF excitation wavelengths (AF360-AF430) and for the 3 CEFS D1 (a), D2 (b), D3 (c).

Fig. 8
Fig. 8

Recognition rate of the OVA as a function of the 7 combinations of (Di)1⩽i⩽3 distances and the 3 modalities: AF alone (black bar), DR alone (grey bar) and AF+DR together (light grey bar), using (left) DCT method, (center) MI method and (right) hybrid method

Tables (4)

Tables Icon

Table 1 P-value for different combinations

Tables Icon

Table 2 Confusion matrices of the M-SVM2 calculated from DCT-based extraction/selection applied to AF spectra (top), from MI-based selection applied to DR spectrum (middle) and from hybrid method (both AF-DCT and DR-MI) (bottom) of spectra acquired at D1, D2 and D3. The 3 matrices on the left correspond to test performance given for D1 alone and the 3 matrices on the right provide test performance given for D1D2D3 together.

Tables Icon

Table 3 Confusion matrices of the M-SVM2 calculated from hybrid method (both AF-DCT and DR-MI) of spectra acquired at D1 (on the left) and of spectra acquired at D1D2D3 together (on the right), using a linear kernel.

Tables Icon

Table 4 Confusion matrices of the M-SVM2 calculated from hybrid method (both AF-DCT and DR-MI) of spectra acquired at D1 (on the left) and of spectra acquired at D1D2D3 together (on the right), using a linear kernel.

Equations (5)

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

x c ( k ) = w ( k ) n = 1 N x ( n ) cos [ π ( 2 n 1 ) ( k 1 ) 2 N ]
M I ( X , Y ) = x 𝒳 y 𝒴 p ( x , y ) log p ( x , y ) p 1 ( x ) p 2 ( y ) .
H ( X | Y ) = y 𝒴 p 2 ( y ) x 𝒳 p ( x | y ) log p ( x | y ) .
M I ( X , Y ) = H ( X ) H ( X | Y ) .
κ ( x , x ) = exp ( μ x x 2 )

Metrics