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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.

While the popularity of MATLAB for scientific and engineering applications is unabated, its poor performance compared to traditional languages, such as Fortran or even C, for a general class of problems continues to impede its deployment in full-scale simulations and data analysis. To ameliorate performance, we have been developing a MATLAB and Octave compiler that leverages the interpreter to implement some of the optimizations as concrete partial evaluations. Specifically, this paper describes constant propagation and type inference, using a high-level tree-transformation tool that has built-in support for solving dataflow problems. The approach allows propagation and folding of constants in cases that would be impractically difficult otherwise. The idea, when extended to infer variable types, provides a natural way to disambiguate types at compile time while leaving the fallback code in place for run-time evaluation. Experimental evaluation on pieces of real MATLAB code demonstrates the effectiveness of the approach.