Image analysis in non-linear microscopy
Artikel i vetenskaplig tidskrift, 2008
The ability to automatically extract quantitative data from non-linear microscopy images is here explored, taking their non-linear character into account. Objects of different degree of complexity were investigated: theoretical images of spherical objects, experimentally collected Coherent Anti-Stokes Raman Scattering images of polystyrene spheres in background generating agar, well-separated lipid droplets in living yeast cells and conglomerations of lipid droplets in living C. elegans nematodes. The in linear microscopy useful measure of Full-Width-at-Half-Maximum (FWHM) was shown to provide inadequate measures of object size due the non-linear density dependence of the signal. Instead, the capability of four state-of-the-art image analysis algorithms was evaluated. Among these Local thresholding was found to be the widest applicable segmentation algorithm.