The normalized cut algorithm is a graph partitioning algorithm that has previously been used successfully for image segmentation. It is originally applied to pixels by considering each pixel in the image as a node in the graph. In this paper we investigate the feasibility of applying the normalized cut algorithm to micro segments by considering each micro segment as a node in the graph. This will severely reduce the computational demand of the normalized cut algorithm, due to the reduction of the number of nodes in the graph. The foundation of the translation to micro segments will be the region adjacency graph. A floating point based rainfalling watershed algorithm will create the initial micro segmentation. We will first explain the rainfalling watershed algorithm. Then we will review the original normalized cut algorithm for image segmentation and describe the changes that are needed when we apply the normalized cut algorithm to micro segments. We investigate the noise robustness of the complete segmentation algorithm on an artificial image and show the results we obtained on photographic images. We also illustrate the computational demand reduction by comparing the running times.