Fast Detection of Cells Using a Continuously Scalable Mexican-Hat-Like Template

We propose a fast algorithm for the detection of cells in fluorescence images. The algorithm, which estimates the number of cells and their respective centers and radii, relies on the fast computation of intensity-based correlations between the cells and a near-isotropic Mexican-hat-like detector. The attractive features of our algorithm are its speed and accuracy. The former attribute is derived from the fact that we can compute correlations between a cell and detectors of various sizes using O(1) operations; whereas, it is our ability to continuously control the center and the radius of the detector that results in a precise estimate of the position and size of the cell. We provide experimental results on both simulated and real data to demonstrate the speed and accuracy of the algorithm.