Summary: The goal of this communication is to suggest an alternative implementation of the $k$-way Ncut approach for image segmentation. We believe that our implementation alleviates a problem associated with the Ncut algorithm for some types of images: its tendency to partition regions that are nearly uniform with respect to the segmentation parameter. Previous implementations have used the $k$-means algorithm to cluster the data in the eigenspace of the affinity matrix. In the $k$-means based implementations, the number of clusters is estimated by minimizing a function that represents the quality of the results produced by each possible value of $k$. Our proposed approach uses the clustering algorithm of Koontz and Fukunaga in which $k$ is automatically selected as clusters are formed (in a single iteration). We show comparison results obtained with the two different approaches to non-parametric clustering. The Ncut generated oversegmentations are further suppressed by a grouping stage ‒ also Ncut based ‒ in our implementation. The affinity matrix for the grouping stage uses similarity based on the mean values of the segments.