Model Convolution: A Computational Approach to Digital Image Interpretation

• Published in: Cellular and molecular bioengineering Vol. 3; no. 2; pp. 163 - 170
• Main Authors: Gardner, Melissa K., Sprague, Brian L., Pearson, Chad G., Cosgrove, Benjamin D., Bicek, Andrew D., Bloom, Kerry, Salmon, E. D., Odde, David J.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.06.2010; Springer Nature B.V
• Subjects: Biological and Medical Physics; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biophysics; Cell Biology; Computers; Convolution; Digital imaging; Engineering; Fluorescence; Fluorescence microscopy; Microscopy; Spatial distribution; Fluorescence; Deconvolution; Model-convolution; Microscopy; Modeling
• ISSN: 1865-5025; 1865-5033
• DOI: 10.1007/s12195-010-0101-7

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.