In MRI, image intensity non-uniformity is an adverse phenomenon that increases inter-tissue overlapping. The aim of this study was to provide a novel general framework, named regularized feature condensing (RFC), for condensing the distribution of image features and apply it to correct intensity non-uniformity via spatial regularization. The proposed RCF method is an iterative procedure, which consists of four basic steps. First, creation of a feature space, which consists of multi-spectral image intensities and corresponding second derivatives. Second, estimation of the intensity condensing map in feature space, i.e. the estimation of the increase of feature probability densities by a well-established mean shift procedure. Third, regularization of intensity condensing map in image space, which yields the estimation of intensity non-uniformity. Fourth, applying the estimation of non-uniformity correction to the input image. In this way, the intensity distributions of distinct tissues are gradually condensed via spatial regularization. The method was tested on simulated and real MR brain images for which gold standard segmentations were available. The results showed that the method did not induce additional intensity variations in simulated uniform images and efficiently removed intensity non-uniformity in real MR brain images. The proposed RCF method is a powerful fully automated intensity non-uniformity correction method that makes no a prior assumptions on the image intensity distribution and provides non-parametric non-uniformity correction.