Blind Source Separation Based on ICA Algorithm Applied to Multispectral Fluorescence Imaging
Sana Lafi, Ali Khalfallah, Mohamed Bouzid, André Bouchot, Med Salim Bouhlel · International Review on Computers and Software (IRECOS) · 2016
Immunofluorescence is one of the most used techniques in optical fluorescence microscopy and has substantial applications in biology and pathology. It aims to detect and localize one or more proteins thanks to the use of specific dyes. One of the major issues with immunofluorescence is the intrinsic fluorescence, called auto-fluorescence present in some biological specimen. Several approaches, based on blind source deconvolution, are developed to deal with this problem and to isolate the extrinsic fluorescence from the intrinsic one. In this paper, we present two Independent Component Algorithms based on second order and higher order statistics. Experimental results have revealed that the blind identification algorithm based on second-order statistics is more adaptedly fit to fluorescence sources un-mixing problem than algorithms based on higher-order statistics.