A Graph Multi-separator Problem for Image Segmentation

Abstract We propose a novel abstraction of the image segmentation task in the form of a combinatorial optimization problem that we call the multi-separator problem . Feasible solutions indicate for every pixel whether it belongs to a segment or a segment separator, and indicate for pairs of pixels whether or not the pixels belong to the same segment. This is in contrast to the closely related lifted multicut problem, where every pixel is associated with a segment and no pixel explicitly represents a separating structure. While the multi-separator problem is np -hard, we identify two special cases for which it can be solved efficiently. Moreover, we define two local search algorithms for the general case and demonstrate their effectiveness in segmenting simulated volume images of foam cells and filaments.

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Publication Details

Journal
Journal of Mathematical Imaging and Vision
Published
2024-08-12
DOI
https://doi.org/10.1007/s10851-024-01201-1
Citations
1
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
0.25

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A Graph Multi-separator Problem for Image Segmentation

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article

A Graph Multi-separator Problem for Image Segmentation

Mark Schöne, Jannik Irmai, Bjoern Andres, Jannik Presberger, Shengxian Zhao
article en
1 citations

Abstract

Abstract We propose a novel abstraction of the image segmentation task in the form of a combinatorial optimization problem that we call the multi-separator problem . Feasible solutions indicate for every pixel whether it belongs to a segment or a segment separator, and indicate for pairs of pixels whether or not the pixels belong to the same segment. This is in contrast to the closely related lifted multicut problem, where every pixel is associated with a segment and no pixel explicitly represents a separating structure. While the multi-separator problem is np -hard, we identify two special cases for which it can be solved efficiently. Moreover, we define two local search algorithms for the general case and demonstrate their effectiveness in segmenting simulated volume images of foam cells and filaments.

Journal of Mathematical Imaging and VisionVol. 66(5)
Technische Universität Dresden (DE)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 49%
Medical Image Segmentation Techniques
0.25
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A Graph Multi-separator Problem for Image Segmentation — Mark Schöne, Jannik Irmai, et al. · Journal of Mathematical Imaging and Vision (2024) | TGRS Research Map | TGRS