A Hypothesis Testing Approach for Fluorescent Blob Identification

Template matching is a common approach for identifying fluorescent objects within a biological image. But how to decide a threshold value for the purpose of justifying the goodness of matching score is a rather difficult task. In this paper, we propose a framework that dynamically chooses appropriate threshold values for correct object identification at a non-arbitrary statistical power based on the local measure of signal and noise. We validate the feasibility of our proposed framework by presenting simulation experiments conducted with both synthetic and live-cell data sets. The experimental results suggest that our auto-thresholding algorithm and local signal to noise ratio estimation can provide solid means for effective spot identity in place of an ad hoc threshold fitting value or minimization method.