Heuristic Linking Models in Multiscale Image Segmentation

This paper presents a novel approach to multiscale image segmentation. It addresses the linking of pixels at adjacent levels in scale-space and the labeling of roots representing segments in the original image. In previous multiscale segmentation approaches, linking and root labeling were based on intensity proximity only. The approach proposed here contains multiple heuristic mechanisms that result in a single criterion for linking ( affection) and root labeling ( adultness). The segmentations are validated by measuring the amount of postprocessing that is needed to reach an objectively defined accuracy of segmentation. The evaluation is performed using three artificial 2D images with different characteristics, and two 2D magnetic resonance brain images. A comparison is made with a pyramid segmentation method. It is found that several of the proposed heuristic link and root mechanisms improve the performance of multiscale segmentation. A very satisfactory segmentation of all images could be obtained by using a fixed set of compromised weight settings of the most effective mechanisms.