OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 1 — Jan. 2, 2012
  • pp: 228–244

Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy

F. Abdat, M. Amouroux, Y. Guermeur, and W. Blondel  »View Author Affiliations

Optics Express, Vol. 20, Issue 1, pp. 228-244 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (1215 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



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

OCIS Codes
(070.4790) Fourier optics and signal processing : Spectrum analysis
(070.5010) Fourier optics and signal processing : Pattern recognition
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(070.2025) Fourier optics and signal processing : Discrete optical signal processing

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: August 17, 2011
Revised Manuscript: November 21, 2011
Manuscript Accepted: November 24, 2011
Published: December 20, 2011

Virtual Issues
Vol. 7, Iss. 3 Virtual Journal for Biomedical Optics

F. Abdat, M. Amouroux, Y. Guermeur, and W. Blondel, "Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy," Opt. Express 20, 228-244 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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).
  12. 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]
  13. 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]
  14. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernelbased learning methods (Cambridge University Press, Cambridge, 2000).
  15. 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]
  16. 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]
  17. S. Majumder, N. Ghosh, and P. Gupta, “Support vector machine for optical diagnosis of cancer,” J. Biomed. Opt. 10, 024034 (2005). [CrossRef] [PubMed]
  18. 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]
  19. 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]
  20. 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).
  21. A. M. Sarhan, “Iris recognition using discrete cosine transform and artificial neural networks,” J. Comp. Scienc. 5, 369–373 (2009). [CrossRef]
  22. W. B. Pennebaker and I. J. L. Mitchel, Jpeg still image data compression standard (Van Nostrand Reinhold, New York, NY, 1993).
  23. G. Potamianos, H. P. Graf, and E. Cosatto, “An image transform approach for hmm based automatic lipreading,” IEEE Int. Conf. Image. Process. (1998).
  24. H. Chang and N. S. Kim, “Speech enhancement using warped discrete cosine transform,” Speech Coding. 175–177 (2002).
  25. A. Jain, Fundamentals of Digital Image Processing (Englewood Cliffs, NJ: Prentice-Hall1989).
  26. T. M. Cover and J. Thomas, Elements of information theory (Wiley Series in Telecommunications, New York, 1991). [CrossRef]
  27. 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]
  28. 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]
  29. 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]
  30. M. Anthony and P. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, Cambridge, 1999). [CrossRef]
  31. L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer-Verlag, New York1996).
  32. F. Liang, “An effective bayesian neural network classifier with a comparison study to support vector machine,” Neural Comput. 15, 1959–1989 (2003). [CrossRef]
  33. B. Schölkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” Int. Conf. Knowledge Discov. Data. Mining. 252–257 (1995).
  34. V. Vapnik, The nature of statistical learning theory (Springer-Verlag, New York, 1995).
  35. C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Lear. 20, 273–297 (1995). [CrossRef]
  36. 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).
  37. Y. Guermeur and E. Monfrini., “A quadratic loss multi-class svm for which a radius-margin bound applies,” Informatica 22, 73–96 (2011).
  38. F. Lauer and Y. Guermeur, “MSVMpack: a multi-class support vector machine package”, J. Mach. Lear. Res. 12, 2293–2296 (2011).
  39. Y. Guermeur, “A generic model of multi-class support vector machine,” Int. J. Int. Inf. Datab. Sys. (2011), (accepted).
  40. 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]
  41. M. D. Keller, Optical spectroscopy for the evaluation of surgical margin status following breast cancer resection, (Ph.D. dissertation, Nashville, Tennessee, 2009). [PubMed]
  42. 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]
  43. 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]
  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]
  45. G. Wagnieres, W. Star, and B. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Phot. Chem. Photobiol. 68, 603–32 (1998).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited