A new region-edge based level set model with applications to image segmentation

Xuhao Zhi; Hong-Bin Shen

doi:10.1117/12.2304517

10 April 2018 A new region-edge based level set model with applications to image segmentation

Xuhao Zhi, Hong-Bin Shen

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152A (2018) https://doi.org/10.1117/12.2304517
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Level set model has advantages in handling complex shapes and topological changes, and is widely used in image processing tasks. The image segmentation oriented level set models can be grouped into region-based models and edge-based models, both of which have merits and drawbacks. Region-based level set model relies on fitting to color intensity of separated regions, but is not sensitive to edge information. Edge-based level set model evolves by fitting to local gradient information, but can get easily affected by noise. We propose a region-edge based level set model, which considers saliency information into energy function and fuses color intensity with local gradient information. The evolution of the proposed model is implemented by a hierarchical two-stage protocol, and the experimental results show flexible initialization, robust evolution and precise segmentation.

Citation Download Citation

Xuhao Zhi and Hong-Bin Shen "A new region-edge based level set model with applications to image segmentation", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152A (10 April 2018); https://doi.org/10.1117/12.2304517

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KEYWORDS

Image segmentation

Image processing

Lithium

Mathematical modeling

Sensors

Data modeling

Binary data

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