The tree of shapes is a powerful tool for image representation which holds many interesting properties. There are many works in the literature that use it for image segmentation, but most of them use only boundary information along the level lines. In many real images this is not enough to achieve a good segmentation, and region information must be introduced. In this work we present a novel region-based segmentation algorithm using the tree of shapes. The approach taken consists in the selection of relevant level-lines according to region based descriptors computed from their interior. We describe a region using the histogram of its features and we select interesting regions by identifying parts of the tree with an homogeneous histogram. The main contribution of this work is the joint use of histograms and suitable metrics between them, with the powerful representation of the tree of shapes. This allows us to handle complex region models and thus improves on previous works which were only able to deal with piecewise constant models. We validate our approach with real images and we obtain results which are favorably compared with some well known related approaches.