In many instances of signal and image processing, it is indispensable to precisely distinguish scattered peaks from a background, e.g., camera signals in microscopy. Here we addressed the detection of Gaussian signals in simulated line profiles (LP) comparable with e.g., fluorescence microscopy data. In a first step, we measured the applicability of histogram-based global background estimation. We find that the method is valid for typical scattered Gaussian signals if they are averagely separated by interpeak distances of 5.5 standard deviations. This enabled us to design global background determination-based peak detection (GBPD). GBPD was compared with two local background determination-based signal detection methods that had been designed for analysis of electrophysiological data and microscopy images, respectively. We were able to prove via receiver-operator characteristic (ROC) comparisons of signal-to-noise ratio (SNR), interpeak distance, and filtering behavior that, when applicable, GBPD brings advantages in knowledge needed a priori, performance at any SNR, controllability and spatial resolution.
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