A multiscale circular geodesic model for image segmentation

Xin Chen; Li Liu; ShuWang Zhou; YaLin Wang

doi:10.1117/12.3021401

25 March 2024 A multiscale circular geodesic model for image segmentation

Xin Chen, Li Liu, ShuWang Zhou, YaLin Wang

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130890I (2024) https://doi.org/10.1117/12.3021401
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

It is a popular way to incorporate the active contour evolution scheme into the multiscale image decomposition and reconstruction procedure, so as to enhance the image segmentation accuracy. However, most of these models are carried out by the level set formulation which cost much computation time. In this paper, we propose a new image segmentation model that combines the circular geodesic model with an adaptive cut and the multiscale image processing. As a consequence, the proposed model can blend the benefits from both of the geodesic models and the multiscale image analysis method. Experimental results show that the proposed multiscale geodesic model indeed outperforms the circular geodesic model with an adaptive cut in solving the image segmentation problem in the presence of strong noise.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Chen, Li Liu, ShuWang Zhou, and YaLin Wang "A multiscale circular geodesic model for image segmentation", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130890I (25 March 2024); https://doi.org/10.1117/12.3021401

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