Visualization of localization microscopy data.
Localization microscopy techniques based on localizing single fluorophore molecules now routinely achieve accuracies better than 30 nm. Unlike conventional optical microscopies, localization microscopy experiments do not generate an image but a list of discrete coordinates of estimated fluorophore positions. Data display and analysis therefore generally require visualization methods that translate the position data into conventional images. Here we investigate the properties of several widely used visualization techniques and show that a commonly used algorithm based on rendering Gaussians may lead to a 1.44-fold loss of resolution. Existing methods typically do not explicitly take sampling considerations into account and thus may produce spurious structures. We present two additional visualization algorithms, an adaptive histogram method based on quad-trees and a Delaunay triangulation based visualization of point data that address some of these deficiencies. The new visualization methods are designed to suppress erroneous detail in poorly sampled image areas but avoid loss of resolution in well-sampled regions. A number of criteria for scoring visualization methods are developed as a guide for choosing among visualization methods and are used to qualitatively compare various algorithms.
Baddeley
David
D
Department of Physiology, School of Medical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand.
Cannell
Mark B
MB
Soeller
Christian
C
D000818:Animals; D051244:Caveolin 3 / Q000032:analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D032342:Muscle Cells / Q000737:chemistry; D051381:Rats; D013194:Staining and Labeling / Q000379:methods
D051244:Caveolin 3