Graph partitioning, or graph cut, has been studied by several authors as a way of image segmenting. In the last years, the Normalized Cut has been widely used in order to implement graph partitioning, based on the graph spectra analysis (eigenvalues and eigenvectors). This area is known as Spectral Graph Theory. This work uses a hierarchical structure in order to represent images, the Component Tree. We provide image segmentation based on Normalized Cut, with image representation based on the Component Tree and on its scale-space analysis. Experimental results present a comparison between other image representations, as pixel grids, including multiscale graph decomposition formulation, and Watershed Transform. As the results show, the proposed approach, applied to different images, presents satisfying image segmentation.