Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement

• URL: http://arxiv.org/abs/2008.06909v4
• Date: Thu, 6 May 2021 14:07:42 GMT
• Title: Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement
• Authors: Da Chen, Jian Zhu, Xinxin Zhang, Minglei Shu and Laurent D. Cohen
• Abstract summary: We introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework. The proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
• Score: 19.309722425910465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.

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