Abstract

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.

© 2013 OSA

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. World Health Organization (2008), http://www.who.int/en/
  2. F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
    [CrossRef] [PubMed]
  3. R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
    [CrossRef] [PubMed]
  4. S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
    [CrossRef] [PubMed]
  5. A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
    [CrossRef] [PubMed]
  6. V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
    [CrossRef] [PubMed]
  7. S. C. Kanick, H. J. C. M. Sterenborg, and A. Amelink, “Empirical model of the photon path length for a single fiber reflectance spectroscopy device,” Opt. Express17(2), 860–871 (2009).
    [CrossRef] [PubMed]
  8. G. Zonios and A. Dimou, “Modeling diffuse reflectance from homogeneous semi-infinite turbid media for biological tissue applications: a Monte Carlo study,” Biomed. Opt. Express2(12), 3284–3294 (2011).
    [CrossRef] [PubMed]
  9. S. L. Jacques and S. Prahl, Oregon Medical Laser Center (2010).
  10. J. Glatz, N. C. Deliolanis, A. Buehler, D. Razansky, and V. Ntziachristos, “Blind source unmixing in multi-spectral optoacoustic tomography,” Opt. Express19(4), 3175–3184 (2011).
    [CrossRef] [PubMed]
  11. I. Schelkanova and V. Toronov, “Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head,” Biomed. Opt. Express3(1), 64–74 (2012).
    [CrossRef] [PubMed]
  12. S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
    [CrossRef] [PubMed]
  13. J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
    [CrossRef] [PubMed]
  14. J. L. Semmlow, Biosignal and Biomedical Image Processing: MATLAB-Based Applications (CRC Press, 2004), Chap. 9.
  15. R. Gallardo-Caballero, C. J. García-Orellana, H. M. González-Velasco, and M. Macías-Macías, “Independent component analysis applied to detection of early breast cancer signs,” in Proceeding of 9th International Work-Conference on Artificial Neural Networks (IWANN, San Sebastian, Spain, 2007), pp. 988–995.
  16. I. Kopriva and A. Peršin, “Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation,” Med. Image Anal.13(3), 507–518 (2009).
    [CrossRef] [PubMed]
  17. F. Abu-Amara and I. Abdel-Qader, “Detection of breast cancer using independent component analysis,” in Proceedings of IEEE International Conference on Electro/Information Technology (Institute of Electrical and Electronics Engineers, New York, 2007), pp.428–431.
    [CrossRef]
  18. A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
    [CrossRef] [PubMed]
  19. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Netw.13(4-5), 411–430 (2000).
    [CrossRef] [PubMed]
  20. A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw.10(3), 626–634 (1999).
    [CrossRef] [PubMed]
  21. R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
    [CrossRef]

2012 (2)

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

I. Schelkanova and V. Toronov, “Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head,” Biomed. Opt. Express3(1), 64–74 (2012).
[CrossRef] [PubMed]

2011 (2)

2010 (1)

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

2009 (6)

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
[CrossRef] [PubMed]

I. Kopriva and A. Peršin, “Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation,” Med. Image Anal.13(3), 507–518 (2009).
[CrossRef] [PubMed]

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
[CrossRef] [PubMed]

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

S. C. Kanick, H. J. C. M. Sterenborg, and A. Amelink, “Empirical model of the photon path length for a single fiber reflectance spectroscopy device,” Opt. Express17(2), 860–871 (2009).
[CrossRef] [PubMed]

2008 (1)

R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
[CrossRef]

2007 (1)

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

2003 (1)

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

2000 (1)

A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Netw.13(4-5), 411–430 (2000).
[CrossRef] [PubMed]

1999 (1)

A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw.10(3), 626–634 (1999).
[CrossRef] [PubMed]

Acar, E.

R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
[CrossRef]

Amelink, A.

Amita, T.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Bart, J.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Barth, R. J.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

Bro, R.

R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
[CrossRef]

Buehler, A.

Conde, O. M.

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

de Jong, J. S.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

de Vries, J.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Dehghani, H.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Deliolanis, N. C.

Dimou, A.

Fitzal, F.

F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
[CrossRef] [PubMed]

Garcia-Allende, P. B.

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

Gibson, J. J.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Glatz, J.

Graafland, M.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Hasan, T.

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

Hoopes, P. J.

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

Hyvärinen, A.

A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Netw.13(4-5), 411–430 (2000).
[CrossRef] [PubMed]

A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw.10(3), 626–634 (1999).
[CrossRef] [PubMed]

Ishikawa, A.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Jakesz, R.

F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
[CrossRef] [PubMed]

Jiang, S.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Kanick, S. C.

Kogel, C.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Kohno, S.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Kolda, T. G.

R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
[CrossRef]

Kopriva, I.

I. Kopriva and A. Peršin, “Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation,” Med. Image Anal.13(3), 507–518 (2009).
[CrossRef] [PubMed]

Krishnaswamy, V.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

Laughney, A. M.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

Meriläinen, P.

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
[CrossRef] [PubMed]

Miyai, I.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Noponen, T.

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
[CrossRef] [PubMed]

Ntziachristos, V.

O’Hara, J. A.

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

Oda, I.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Oja, E.

A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Netw.13(4-5), 411–430 (2000).
[CrossRef] [PubMed]

Paulsen, K. D.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Peršin, A.

I. Kopriva and A. Peršin, “Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation,” Med. Image Anal.13(3), 507–518 (2009).
[CrossRef] [PubMed]

Pleijhuis, R. G.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Pogue, B. W.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Poplack, S. P.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Razansky, D.

Riedl, O.

F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
[CrossRef] [PubMed]

Rizzo, E. J.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

Samkoe, K. S.

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

Schelkanova, I.

Schwab, M. C.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

Seiyama, A.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Shimizu, K.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

Soho, S.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Srinivasan, S.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Sterenborg, H. J. C. M.

Toronov, V.

Tosteson, T. D.

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Tsuneishi, S.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

van Dam, G. M.

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Virtanen, J.

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
[CrossRef] [PubMed]

Wells, W. A.

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

Zonios, G.

Ann. Surg. Oncol. (1)

R. G. Pleijhuis, M. Graafland, J. de Vries, J. Bart, J. S. de Jong, and G. M. van Dam, “Obtaining adequate surgical margins in breast-conserving therapy for patients with early-stage breast cancer: current modalities and future directions,” Ann. Surg. Oncol.16(10), 2717–2730 (2009).
[CrossRef] [PubMed]

Biomed. Opt. Express (2)

Clin. Cancer Res. (1)

A. M. Laughney, V. Krishnaswamy, E. J. Rizzo, M. C. Schwab, R. J. Barth, B. W. Pogue, K. D. Paulsen, and W. A. Wells, “Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment,” Clin. Cancer Res.18(22), 6315–6325 (2012).
[CrossRef] [PubMed]

IEEE Trans. Neural Netw. (1)

A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw.10(3), 626–634 (1999).
[CrossRef] [PubMed]

J. Biomed. Opt. (4)

V. Krishnaswamy, P. J. Hoopes, K. S. Samkoe, J. A. O’Hara, T. Hasan, and B. W. Pogue, “Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy,” J. Biomed. Opt.14(1), 014004 (2009).
[CrossRef] [PubMed]

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt.12(6), 062111 (2007).
[CrossRef] [PubMed]

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt.14(5), 054032 (2009).
[CrossRef] [PubMed]

A. M. Laughney, V. Krishnaswamy, P. B. Garcia-Allende, O. M. Conde, W. A. Wells, K. D. Paulsen, and B. W. Pogue, “Automated classification of breast pathology using local measures of broadband reflectance,” J. Biomed. Opt.15(6), 066019 (2010).
[CrossRef] [PubMed]

J. Chemometr. (1)

R. Bro, E. Acar, and T. G. Kolda, “Resolving the sign ambiguity in the singular value decomposition,” J. Chemometr.22(2), 135–140 (2008).
[CrossRef]

Langenbecks Arch. Surg. (1)

F. Fitzal, O. Riedl, and R. Jakesz, “Recent developments in breast-conserving surgery for breast cancer patients,” Langenbecks Arch. Surg.394(4), 591–609 (2009).
[CrossRef] [PubMed]

Med. Image Anal. (1)

I. Kopriva and A. Peršin, “Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation,” Med. Image Anal.13(3), 507–518 (2009).
[CrossRef] [PubMed]

Neural Netw. (1)

A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Netw.13(4-5), 411–430 (2000).
[CrossRef] [PubMed]

Opt. Express (2)

Proc. Natl. Acad. Sci. U.S.A. (1)

S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. Natl. Acad. Sci. U.S.A.100(21), 12349–12354 (2003).
[CrossRef] [PubMed]

Other (5)

World Health Organization (2008), http://www.who.int/en/

S. L. Jacques and S. Prahl, Oregon Medical Laser Center (2010).

F. Abu-Amara and I. Abdel-Qader, “Detection of breast cancer using independent component analysis,” in Proceedings of IEEE International Conference on Electro/Information Technology (Institute of Electrical and Electronics Engineers, New York, 2007), pp.428–431.
[CrossRef]

J. L. Semmlow, Biosignal and Biomedical Image Processing: MATLAB-Based Applications (CRC Press, 2004), Chap. 9.

R. Gallardo-Caballero, C. J. García-Orellana, H. M. González-Velasco, and M. Macías-Macías, “Independent component analysis applied to detection of early breast cancer signs,” in Proceeding of 9th International Work-Conference on Artificial Neural Networks (IWANN, San Sebastian, Spain, 2007), pp. 988–995.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (8)

Fig. 1
Fig. 1

ICA-PCA analysis process on the reflectance images.

Fig. 2
Fig. 2

ICA discriminant results with approximate scale bar: (a) and (b) are both correct solutions due to sign ambiguity, where the magenta (strongest tone) are values with positive signs. When surgeon selects some points of the image where the pathology is sure (circles on (c)) the region of malignancy (striped ROI) can be defined as a positive (magenta) region, avoiding the sign ambiguity.

Fig. 3
Fig. 3

Example of kept variance for different tissue samples: sample 1 (black) consists of normal-adipose tissue combination; sample 2 (red) consists of malignant-adipose tissue.

Fig. 4
Fig. 4

Mean spectral variations of PC1, PC2 and PC3 coefficients on matrix (A) for the whole number of breast samples, where “prop.” means proportional.

Fig. 5
Fig. 5

Blind analysis and physical properties of scattering with an approximation of the scale bar: (a) scattering power map from model fitting; (b) score of PC2; (c) scatter plot showing their correlation.

Fig. 6
Fig. 6

Blind analysis and physical properties of absorption: (a) digital photograph with blood points in black circles and approximate scale bars, (b) Coefficient, (eigenvector) of the 3th principal component, with its spectral variation, (c) PC3 score.

Fig. 7
Fig. 7

IC selection based on correlation with the digital photograph of a malignant sample. Circles correspond to the areas identified as malignant by the pathologist, scale bars are approximated. (a) Digital photograph of the breast specimen; (b) last IC with its correlation and probability of detection; (c) penultimate IC with its correlation and probability of detection; (d) H&E section of the sample.

Fig. 8
Fig. 8

Tumor diagnosis results, where the pathologist evaluation is shown with different colored circles and the scale bar is an approximation: (a) photograph image; (b) co-registered H&E section; (c) probability of tumor map.

Tables (4)

Tables Icon

Table 1 Regions of Interest (ROI) Diagnosed by the Pathologist on the 29 Specimens

Tables Icon

Table 2 Correlation Study To Determine the Hemoglobin Presence on Each Principal Component*

Tables Icon

Table 3 Separation Between Normalized Mean Data from Different Pathologies Depending on the Score Chosen for the Diagnosis

Tables Icon

Table 4 Comparison Between the Outcomes of Sensitivity and Specificity of a Classifier of Malignancy for the Different BSS Strategies and a Supervised Technique as KNN

Equations (8)

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

R( λ )=A  λ -b exp[ -ρ  C HbT ( f O 2 ε Hb O 2 ( λ )+( 1- f O 2 ) ε Hb ( λ ) ) ]
s M×N = W M×M   x M×N
x M×N = W M×M 1   s M×N =  A M×M   s M×N
X( λ )=ln( R( λ ) ) = ln( A )-b ln( λ ) -ρ  C HbT [ f O 2 ε Hb O 2 ( λ )-( 1-  f O 2 ) ε Hb ( λ )] =  S 1 σ 1 ( λ )+ S 2 σ 2 ( λ )+ S 3 σ 3 ( λ )
X(λ,N)= x M×N = σ M×M s M×N = W M×M 1 s M×N = A M×M s M×N
C=  x  x T  = E D  E T 
Keptvariance= q=1 L D(q,q) q=1 M D(q,q) 100( % )threshold
ln( R( λ ) )= p c 1 - p c 2  ln( λ ) -p c 2  K( λ )

Metrics