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A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations
Yu, Sha; Lu, Yao; Molloy, Derek
This paper proposes a dynamic-shape-prior guided snake (DSP G-snake) model that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake’s snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from selfcrossing, or automatically untie an already self-intersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as the state-of-the-art cell tracking frameworks.
Keyword(s): Algorithms; Image processing; Biomedical engineering; Snakes; active contour; self-intersection; cell population tracking; dynamic shape prior; global-topology regularity
Publication Date:
2018
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Yu, Sha ORCID: 0000-0001-9796-1446 <https://orcid.org/0000-0001-9796-1446>, Lu, Yao ORCID: 0000-0003-3478-802X <https://orcid.org/0000-0003-3478-802X> and Molloy, Derek (2018) A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations. IEEE Transactions on Image Processing, 28 (182416). pp. 1513-1527. ISSN 1941-0042
Publisher(s): Institute of Electrical and Electronics Engineers (IEEE)
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/24907/1/TIP2019_DSP-GSnake_ShaYu_acceptedV.pdf,
http://dx.doi.org/10.1109/TIP.2018.2878331
First Indexed: 2020-08-01 07:13:50 Last Updated: 2020-08-01 07:13:50