Image analysis in non-linear microscopy
Journal article, 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.

Yeast

Non-linear optics

Image analysis

C. elegans

CARS microscopy

Author

Jonas Hagmar

Christian Brackmann

Chalmers, Chemical and Biological Engineering, Molecular Imaging

Tomas Gustavsson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Annika Enejder

Chalmers, Chemical and Biological Engineering, Molecular Imaging

Journal of Optical Society of America

Vol. 25 2195-2206

Subject Categories

Biological Sciences

Atom and Molecular Physics and Optics

Other Physics Topics

Zoology

Computer Vision and Robotics (Autonomous Systems)

More information

Created

10/8/2017