Thresholding is an example of where the
user can bias the measurement, an equally important issue is which, of all the possible
images, are selected for analysis.
Solutions include
1) blinding – the user is unaware of the experimental source of
the images at both the acquisition and analysis stages. It is also worth mixing
up the different experimental images to avoid any bias caused by drift in the
users technique. While this does not eliminate bias introduced by individual
users it does reduce the influence of an individual user over a whole dataset.
2) declaring and publishing clear criteria by which cells were
selected/rejected. This is especially important when single images are
published without any supporting measurements. In 10 000 cells you can
find pretty much anything.
Dr
F451a
Cell Biologi
Wenner-Gren
Inst.
The
Arhenius Lab
S-106 91
tel +46 (0)8 16 2759
From:
Sent: den 22 juni 2009 02:28
To:
[hidden email]
Subject: Auotmatic thresholding
I
have a question regarding thresholding. Do users have much success with
automatic thresholding of samples? We have Image Pro Plus and
anlayse samples with variable size ranges and signal intensities. We
manually threshold very carefully with enhancement features but it would be
nice for this to be done automatically with a robust method but I am not sure
how to do this and be confident with the results.
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