.png){kind=icon}
Return
A Generalized Geodesic Voting Framework for Interactive Image Segmentation
.png){kind=icon}
OA
AI
DOI：10.1109/TIM.2024.3502832.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we introduce a new variational model for addressing the image segmentation problem of minimal user interaction. The proposed variational segmentation model, regarded as a variant of the classical geodesic voting method, is designed to take advantage of the topology constraint of object boundary contour and the local homogeneity of image gray levels. In particular, the proposed model takes into account a metric of the asymmetric quadratic form to build the path energy of the boundary contour, allowing it to encode the local image intensity consistency for computing minimal paths. Furthermore, an adaptive cut-based closed contour computation scheme is invoked to depict the target boundary contour, implemented by computing two minimal paths traveling along two different directions from a single source point located at the adaptive cut. Both paths form a simple closed curve that links the two sides of the cut. Finally, experimental results show that the proposed geodesic voting model can deal with complicated segmentation tasks and indeed outperforms state-of-the-art image segmentation approaches, especially for medical image segmentation problems.
Keywords:
Image segmentation
Computational modeling
Mathematical models
Measurement
Feature extraction
Analytical models
Numerical models
Biomedical imaging
Active contours
Image edge detection
geodesic models
geodesic voting
Hamilton-Jacobi-Bellman (HJB) equation
image segmentation
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveReliability-based design optimization of axial compressor using uncertainty model for stall margin
PREAI
ShareSaveShareSave
View more