Christoph Florian Schaller - FU Berlin, FB MI

Christoph Schaller - STORMicroscopy 31
7.2 Single bead
Second we observe and localize a large single bead in STORM image #1 for 100 frames, being interested
in the t accuracy of the two already compared algorithms, numerical integrations(NI) and Gaussian
mask tting(GM).
The adjacent Figure 7.3 shows the
distribution of the found spot centers
for both algorithms. Calculations yield
σ NI = 2.76 and σ GM = 3.02 as standard
deviations of the numerical integration
and Gaussian mask ts from
their mean center respectively. Repeating
the analysis using RapidSTORM
results in σ RS = 2.89. This agrees
with our expectations because full least
squares tting (of a pixelated Gaussian)
is applied here. Therefore our
algorithm seems to perform best, primarily
when we take into considera-
Figure 7.3: Distribution of tted centers of a single bead.
tion that the average center of the numerical
integration t should be closer to the true spot center according to our simulations. This is a
result we would like to quantify and conrm in theory.
Prior to this, we want to have a
look at the occuring drift. Thence we
average twenty ts respectively and plot
the movement of those mean spot centers
in Figure 7.4. We clearly see that
for both algorithms a drift of approximately
1.5 nm in x- and 3 nm in y-
direction occurs. Therefore we correct
the inuence of this drift by shifting
every t according to the average position
of the surrounding 20 frames.
Figure 7.5 shows the drift-corrected
spot centers in comparison to the previous
Figure 7.3. We obtain considerably
improved standard deviations of
Figure 7.4: Drift of a single bead.
σ NI = 2.51 and σ GM = 2.75. To be able to relate this to the results of our simulations, we calculate
the average errors (R 2 distances from the mean center), too. A comparison is shown in the adjacent
Table 7.1.
Table 7.1: Average errors for tting a single bead.