Automated super-resolution detection of fluorescent rods in 2D
We describe a method designed to detect fluorescent rods from 2D microscopy images. It is motivated by the desire to study the dynamics of bacteria such as Shigella. The methodology is adapted from a super-resolution spot detection algorithm and is based on a parametric model of the rods and the microscope PSF. The algorithm consists of two parts: 1) a pre-detection step, based on thresholding a score computed from the product of the mean curvature and the local intensity of the filtered image, 2) an iterative procedure, where a mixture model of blurred segments is fitted to the image, and segments are removed, then added under the control of hypothesis tests. An upper bound is provided for the probability of erroneously detecting rods in noise. We show that the algorithm can reliably detect and accurately localize rods from low SNR images and can distinguish rods separated by subresolution distances. We also illustrate its ability to identify and separate overlapping bacteria on a real image