The ability to automatically extract quantitative data from nonlinear microscopy images is here explored, taking nonlinear and coherent effects into account. Objects of different degrees 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 to the nonlinear 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.
© 2008 Optical Society of America
Original Manuscript: March 17, 2008
Revised Manuscript: June 21, 2008
Manuscript Accepted: June 24, 2008
Published: August 6, 2008
Vol. 3, Iss. 11 Virtual Journal for Biomedical Optics
Jonas Hagmar, Christian Brackmann, Tomas Gustavsson, and Annika Enejder, "Image analysis in nonlinear microscopy," J. Opt. Soc. Am. A 25, 2195-2206 (2008)