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Combining mathematical morphology and the Hilbert transform for fully automatic nuclei detection in fluorescence microscopy

Lookup NU author(s): Phillip Jackson, Professor Boguslaw ObaraORCiD


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© Springer Nature Switzerland AG 2019. Accurate and reliable nuclei identification is an essential part of quantification in microscopy. A range of mathematical and machine learning approaches are used but all methods have limitations. Such limitations include sensitivity to user parameters or a need for pre-processing in classical approaches or the requirement for relatively large amounts of training data in deep learning approaches. Here we demonstrate a new approach for nuclei detection that combines mathematical morphology with the Hilbert transform to detect the centres, sizes and orientations of elliptical objects. We evaluate this approach on datasets from the Broad Bioimage Benchmark Collection and compare it to established algorithms and previously published results. We show this new approach to outperform established classical approaches and be comparable in performance to deep-learning approaches. We believe this approach to be a competitive algorithm for nuclei detection in microscopy.

Publication metadata

Author(s): Nelson CJ, Jackson PTG, Obara B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 14th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2019)

Year of Conference: 2019

Pages: 532-543

Online publication date: 31/05/2019

Acceptance date: 02/04/2018

ISSN: 0302-9743

Publisher: Springer Verlag


DOI: 10.1007/978-3-030-20867-7_41

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030208660