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Accurate and reliable nuclei identification is an essential part ofquantification in microscopy. A range of mathematical and machine learningapproaches are used but all methods have limitations. Such limitations includesensitivity to user parameters or a need for pre-processing in classicalapproaches or the requirement for relatively large amounts of training data indeep learning approaches. Here we demonstrate a new approach for nucleidetection that combines mathematical morphology with the Hilbert transform todetect the centres, sizes and orientations of elliptical objects. We evaluatethis approach on datasets from the Broad Bioimage Benchmark Collection andcompare it to established algorithms and previously published results. We showthis new approach to outperform established classical approaches and becomparable in performance to deep-learning approaches. We believe this approachto be a competitive algorithm for nuclei detection in microscopy.

Combining Mathematical Morphology and the Hilbert Transform for Fully Automatic Nuclei Detection in Fluorescence Microscopy