@article{(Open Science Index):https://publications.waset.org/pdf/6018,
	  title     = {Enhancement of m-FISH Images using Spectral Unmixing},
	  author    = {Martin De Biasio and  Raimund Leitner and  Franz G. Wuertz and  Sergey Verzakov and  Pierre J. Elbischger},
	  country	= {},
	  institution	= {},
	  abstract     = {Breast carcinoma is the most common form of cancer
in women. Multicolour fluorescent in-situ hybridisation (m-FISH) is
a common method for staging breast carcinoma. The interpretation
of m-FISH images is complicated due to two effects: (i) Spectral
overlap in the emission spectra of fluorochrome marked DNA probes
and (ii) tissue autofluorescence. In this paper hyper-spectral images of
m-FISH samples are used and spectral unmixing is applied to produce
false colour images with higher contrast and better information
content than standard RGB images. The spectral unmixing is realised
by combinations of: Orthogonal Projection Analysis (OPA), Alterating
Least Squares (ALS), Simple-to-use Interactive Self-Modeling
Mixture Analysis (SIMPLISMA) and VARIMAX. These are applied
on the data to reduce tissue autofluorescence and resolve the spectral
overlap in the emission spectra. The results show that spectral unmixing
methods reduce the intensity caused by tissue autofluorescence by
up to 78% and enhance image contrast by algorithmically reducing
the overlap of the emission spectra.},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {261 - 267},
	  ee        = {https://publications.waset.org/pdf/6018},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},
	}