Prior-based Hierarchical Segmentation Highlighting Structures of Interest
This work addresses a domain-specific need in image analysis by integrating prior knowledge into segmentation, though it appears incremental as it builds on existing hierarchical and prior-based approaches.
The paper tackles the problem of incorporating prior spatial information into hierarchical image segmentation to emphasize structures of interest, resulting in a versatile method that preserves important image structures across scales.
Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.