Hierarchical image segmentation provides a set of image seg-mentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency.