This work’s general goal is to evaluate the relative impact of using different morphological attributes on the segmentation of different images and object classes. Therefore, this work proposes an extension to the Multiresolution Segmentation method (Baatz00), in a way that several morphological attributes can be considered in the region growing process. In order to select a segmentation quality assessment metric to be used in the evaluation of the proposed segmentation algorithm, a study on eight metrics available in the literature was conducted. This study aimed at assessing the relative performance of the quality metrics and to verify which of them presented the higher correlation with the human perception of segmentation quality. Eight shape attributes were then chosen to compose the heterogeneity criterion and the quality of segmentations using one shape attribute at a time was compared with the color only based segmentation. After that, the impact of using pairs of morphological attributes was also evaluated. The experiments were performed over fifteen classes of interest present in twelve different images, representing application areas such as remote sensing, microscopy and medical images. The results confirm the importance of including morphological attributes in the segmentation process and promote an interesting discussion about future works.