Superpixel-based active contour model via a local similarity factor and saliency

The region-based active contour models could present difficulties because of undesired initial contour, noise distribution and image weak edges. In order to overcome the above problems, this paper proposes a superpixel-based via a local similarity factor and saliency (SLSFS). Firstly, the initial co...

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Veröffentlicht in:Measurement : journal of the International Measurement Confederation 2022-01, Vol.188, p.110442, Article 110442
Hauptverfasser: Liu, Guoqi, Jiang, You, Chang, Baofang, Liu, Dong
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Sprache:eng
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Zusammenfassung:The region-based active contour models could present difficulties because of undesired initial contour, noise distribution and image weak edges. In order to overcome the above problems, this paper proposes a superpixel-based via a local similarity factor and saliency (SLSFS). Firstly, the initial contour is generated by combining super-pixel and fuzzy c-means clustering. Secondly, the difference between local space and local intensity is used to improve the segmentation accuracy under noise. Finally, the weak edge information is protected by improved saliency detection. In addition, a gradient similarity constraint is used to remove the redundant regions. SLSFS model can generate adaptive initial contour around the target, and protect the weak edge information of the target on the premise of ensuring certain noise robustness. Experiments show that the average dice of SLSFS is 14% higher than that of the optimal comparison model and 19% on gray images. [Display omitted] •Initial contour is adaptively generated by combining super-pixel and fuzzy c-means clustering.•Improved saliency detection is introduced into the model, which avoid the segmentation result from being over-smoothing and weak edge leakage.•A local similarity factor is introduced to improve noise robustness and outlier resistance of the model.•The proposed method improves the segmentation robustness compared with the tested methods.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2021.110442