Fluorescence microscopy plays an important role in the classification of cancerous Tissue. The dramatic increase in multicolor fluorescence microscopy applications witnessed over the past decade is due, in part, to the significant advances in instrument and detector design. A number of advanced microscopy techniques have been applied using multi-color fluorescence labeling, including fluorescence recovery after photo bleaching (FRAP), fluorescence correlation spectroscopy (FCS), fluorescence resonance energy transfer (FRET), fluorescence in situ hybridization (FISH), and fluorescence lifetime imaging (FLIM). Many of these methods benefit significantly from the ability to use specifically targeted fluorescent proteins in live-cell imaging experiments. In addition, live-cell imaging has been revolutionized by the introduction of ever increasingly useful genetically encoded fluorescent proteins spanning the entire visible spectral region. However, the problem of fluorescence microscopy is the crosstalk between the channels caused by the overlap of the emission spectra of the different fluorophores, The crosstalk cannot be solved on the filter level, and not by specialized florophores. To eliminate the crosstalk the hyperspectral imaging using the spectra unmixing (algorithmically reduce the overlap of spectra) can be the possible way to reduce the errors in the classification of the tissue. Spectral imaging is the combination of commuter vision and spectroscopy. In addition, because every object of interest consists of more than one pixels, every pixel is dependent on its neighboring pixels. Thus, the spatial context of the image contains useful information for a classification and increase the sensitivity and specificity of a spectral classification.
Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing, American Journal of Biomedical and Life Sciences. Special Issue:Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”.
Vol. 3, No. 2-3,
2015, pp. 1-7.
N. Harbeck, W. Eiermann, J. Engel, I. Funke, A. Lebeau, W. Parmanetter, and M. Mutch. Prognosefaktoren beim primaeren Mammakarzinom, Vol 8, Zuckerschwendt, Muenchen, Bern, Wien, New York, 2001.
R. Lupu, M. Cardillo, M. Hijazi, L. Harrisl and K. Rosenberg. Interaction between erbbreceptors and heregulin in breast cancer tumor progression and drug resistance. Sem Cancer Biol, 6: 135-145, 1995
Z. Mitri, T. Constantine, R. O'Regan (2012). "The HER2 Receptor in Breast Cancer: Pathophysiology, Clinical Use, and New Advances in Therapy". Chemother Res Pract 2012: 743193.
J. R. Lakowicz. Principles of Fluorescence Spectroscopy, volume 3. Springer Media LLC, 233 Spring Street, New York, NY 10013, USA, 2006.
L Coussens, TL Yang-Feng, YC Liao, E Chen, A Gray, J McGrath, PH Seeburg, TA Libermann, J Schlessinger, U Francke (December 1985). "Tyrosine kinase receptor with extensive homology to EGF receptor shares chromosomal location with neu oncogene". Science 230 (4730): 1132–9.
V Roy, EA Perez (November 2009). "Beyond trastuzumab: small molecule tyrosine kinase inhibitors in HER-2-positive breast cancer". Oncologist 14 (11): 1061–9.
I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
M Tan, D Yu (2007). "Molecular mechanisms of erbB2-mediated breast cancer chemoresistance".Adv. Exp. Med. Biol. 608: 119–29
Ji-Won Kim, Jee Hyun Kim, Seock-Ah Im, Yu Jung Kim, Hye-Suk Han, Jin-Soo Kim, Kyung-Hun Lee, Tae-Yong Kim, Sae-Won Han, Yoon Kyung Jeon, Do-Youn Oh, Tae-You Kim, HER2/CEP17 ratio and HER2 immunohistochemistry predict clinical outcome after first-line trastuzumab plus taxane chemotherapy in patients with HER2 fluorescence in situ hybridization-positive metastatic breast cancer, July 2013, Volume 72, Issue 1, pp 109-115 OTSU N. IEEE Trans. Syst. Man Cybern, 1979, 9: 62-66.
Drake, N.A., Mackin, S. and Settle, J.J., 1999, Mapping vegetation,soils, and geology in semiarid shrublands using spectral matching andmixture modelling of SWIR AVIRIS imagery, Remote Sensing ofEnvironment, 67, pp 12-25.
Wessman, C.A., Bateson, C.A. and Benning, T.L., 1997, Detecting fire and grazing patterns in tallgrass prairie using spectral mixture analysis, Ecological Applications, 7, 2, 493-511.
OTSU N. “A Threshold Selection Method from Gray-level Histograms,” IEEE Trans. Syst. Man Cybern, 1979, 9: 62-66.
Path Vysion HER-2 DNA Prob Kit Package insert, Vysis Inc.
G. Viani, S.L. Afonso, E. J. Stefano, L.I. De Fendi and F. V. Soares, Adjuvant trastuzumab in the treatment of HER-2 Positive early breast cancer: a meta analysis of published radioized BMC Cancer [page 7:153, 2007.
Targeted Therapies for Breast Cancer TutorialNational Cancer Institute, at the National Health institute, http://www.cancer.gov/cancertopics/understandingcancer/targetedtherapies/breastcancer_htmlcourse/page3
C. M. Bishop, Pattern Recognition and Machine Learning, Springer; Auflage: first ed. 2006. Corr. 2nd printing 2011 (2007)
R.O.Duda, P.E.Hart and D.G. Strock, Pattern Classification, John Wiley & Sons; Auflage: 2. Auflage (21. November 2000)
T.M. Lillesand, R.W. Kiefer and J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons, Hoboken, NJ, USA, 2004.
Richards, J.A. (1993), Remote Sensing Digital Image Analysis, 2nd ed. Springer Verlag, 1993.
Rissanen, J. (1978), "Modeling by shortest data description," Automatica, vol. 14, pp. 465-471, 1978.
Settle, J.J. (1996), "On the relationship between spectral unmixing and subspace projection," IEEE Trans on Geoscience and Remote Sensing, vol. 34, no. 4, pp. 1045-1046, July 1996.
Settle, J.J. and N.A. Drake (1993), "Linear mixing and estimation of ground cover proportions," Int. J. Remote Sensing, vol. 14, no. 6, pp. 1159-1177, 1993.
Zhao, X. (1996), Subspace Projection Approach to Multispectral!Hyperspectral Image Classification Using Linear Mixture Modeling, Master Thesis, Departrnent of Computer Seiences and Electrical Engineering, University of Maryland Baitimare County, MD, May 1996.
Ren, H. and C.-1 Chang (2000b), "Target-constrained interference-minimized approach to subpixel target detection for hyperspectralimagery," Optical Engineering, vol. 39, no. 12, pp. 3138-3145, December 2000.