Model Convolution: A Computational Approach to Digital Image Interpretation.
Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.
Gardner
Melissa K
MK
Sprague
Brian L
BL
Pearson
Chad G
CG
Cosgrove
Benjamin D
BD
Bicek
Andrew D
AD
Bloom
Kerry
K
Salmon
E D
ED
Odde
David J
DJ
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