Abstract:
:We introduce a nearly automatic procedure to locate and count the quantum dots in images of kinesin motor assays. Our procedure employs an approximate likelihood estimator based on a two-component mixture model for the image data; the first component has a normal distribution, and the other component is distributed as a normal random variable plus an exponential random variable. The normal component has an unknown variance, which we model as a function of the mean. We use B-splines to estimate the variance function during a training run on a suitable image, and the estimate is used to process subsequent images. Parameter estimates are generated for each image along with estimates of standard errors, and the number of dots in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors.
journal_name
Biometricsjournal_title
Biometricsauthors
Hughes J,Fricks Jdoi
10.1111/j.1541-0420.2010.01467.xsubject
Has Abstractpub_date
2011-06-01 00:00:00pages
588-95issue
2eissn
0006-341Xissn
1541-0420pii
BIOM1467journal_volume
67pub_type
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