Image segmentation is concerned with partitioning an image into non-overlapping, constituent regions, which are homogeneous with respect to certain features. In magnetic resonance imaging (MRI), the most discriminative and commonly used features are the image intensities themselves. However, due to noise, partial volume effects, natural and spurious intensity variations, intensity distributions of distinct tissues generally overlap, which makes segmentation difficult and less precise. Using multi-spectral MR images and mapping intensities into a multidimensional feature space may help in segmentation. To further facilitate segmentation, we map the intensities and second derivatives of multi-spectral images into a common multidimensional feature space. Integration of intensity and spatial information may yield complex clusters that cannot be described by Gaussian mixture models or by hyper-spherical shapes. For this reason we devise a novel segmentation method based on non-parametric valley-seeking clustering. The valleys are found by estimating feature density gradients. The proposed segmentation method, with and without spatial information, is tested on simulated and real, single- and multi-spectral, MR brain images. The segmentation results are highly consistent with the gold standard, especially when combined with a procedure for intensity non-uniformity correction, presented in MI 4684-177.