Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing

Issa Ibraheem

doi:doi:10.11648/j.ajbls.s.2015030203.11

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Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing

Issa Ibraheem

Published in American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3)

Received: 7 December 2014 Accepted: 9 December 2014 Published: 7 August 2015

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Abstract

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.

Published in	American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3) This article belongs to the Special Issue Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”
DOI	10.11648/j.ajbls.s.2015030203.11
Page(s)	1-7
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Fluorescence microscopy, for breast cancer, fluorescence in situ hybridization, FISH

References

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APA Style

Issa Ibraheem. (2015). Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. American Journal of Biomedical and Life Sciences, 3(2-3), 1-7. https://doi.org/10.11648/j.ajbls.s.2015030203.11

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ACS Style

Issa Ibraheem. Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. Am. J. Biomed. Life Sci. 2015, 3(2-3), 1-7. doi: 10.11648/j.ajbls.s.2015030203.11

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AMA Style

Issa Ibraheem. Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. Am J Biomed Life Sci. 2015;3(2-3):1-7. doi: 10.11648/j.ajbls.s.2015030203.11

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@article{10.11648/j.ajbls.s.2015030203.11,
  author = {Issa Ibraheem},
  title = {Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing},
  journal = {American Journal of Biomedical and Life Sciences},
  volume = {3},
  number = {2-3},
  pages = {1-7},
  doi = {10.11648/j.ajbls.s.2015030203.11},
  url = {https://doi.org/10.11648/j.ajbls.s.2015030203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.s.2015030203.11},
  abstract = {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.},
 year = {2015}
}

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TY  - JOUR
T1  - Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing
AU  - Issa Ibraheem
Y1  - 2015/08/07
PY  - 2015
N1  - https://doi.org/10.11648/j.ajbls.s.2015030203.11
DO  - 10.11648/j.ajbls.s.2015030203.11
T2  - American Journal of Biomedical and Life Sciences
JF  - American Journal of Biomedical and Life Sciences
JO  - American Journal of Biomedical and Life Sciences
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2330-880X
UR  - https://doi.org/10.11648/j.ajbls.s.2015030203.11
AB  - 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.
VL  - 3
IS  - 2-3
ER  -

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Author Information

Issa Ibraheem

Biomedical Engineering. Al-Andalus Private University for Medical Sciences, Al-Qadmus, Tartus, Syria

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APA Style

Issa Ibraheem. (2015). Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. American Journal of Biomedical and Life Sciences, 3(2-3), 1-7. https://doi.org/10.11648/j.ajbls.s.2015030203.11

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ACS Style

Issa Ibraheem. Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. Am. J. Biomed. Life Sci. 2015, 3(2-3), 1-7. doi: 10.11648/j.ajbls.s.2015030203.11

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AMA Style

Issa Ibraheem. Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing. Am J Biomed Life Sci. 2015;3(2-3):1-7. doi: 10.11648/j.ajbls.s.2015030203.11

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@article{10.11648/j.ajbls.s.2015030203.11,
  author = {Issa Ibraheem},
  title = {Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing},
  journal = {American Journal of Biomedical and Life Sciences},
  volume = {3},
  number = {2-3},
  pages = {1-7},
  doi = {10.11648/j.ajbls.s.2015030203.11},
  url = {https://doi.org/10.11648/j.ajbls.s.2015030203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.s.2015030203.11},
  abstract = {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.},
 year = {2015}
}

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TY  - JOUR
T1  - Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing
AU  - Issa Ibraheem
Y1  - 2015/08/07
PY  - 2015
N1  - https://doi.org/10.11648/j.ajbls.s.2015030203.11
DO  - 10.11648/j.ajbls.s.2015030203.11
T2  - American Journal of Biomedical and Life Sciences
JF  - American Journal of Biomedical and Life Sciences
JO  - American Journal of Biomedical and Life Sciences
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2330-880X
UR  - https://doi.org/10.11648/j.ajbls.s.2015030203.11
AB  - 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.
VL  - 3
IS  - 2-3
ER  -

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